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Updates the requirements on torch, torchvision, matplotlib, pandas, scikit-learn, xgboost, numpy and joblib to permit the latest version.
Updates torch to 2.10.0

Release notes

Sourced from torch's releases.

PyTorch 2.10.0 Release Notes

Highlights

For more details about these highlighted features, you can look at the release blogpost. Below are the full release notes for this release.

Backwards Incompatible Changes

Dataloader Frontend

  • Removed unused data_source argument from Sampler (#163134). This is a no-op, unless you have a custom sampler that uses this argument. Please update your custom sampler accordingly.
  • Removed deprecated imports for torch.utils.data.datapipes.iter.grouping (#163438). from torch.utils.data.datapipes.iter.grouping import SHARDING_PRIORITIES, ShardingFilterIterDataPipe is no longer supported. Please import from torch.utils.data.datapipes.iter.sharding instead.

torch.nn

  • Remove Nested Jagged Tensor support from nn.attention.flex_attention (#161734)

... (truncated)

Changelog

Sourced from torch's changelog.

Releasing PyTorch

Release Compatibility Matrix

Following is the Release Compatibility Matrix for PyTorch releases:

... (truncated)

Commits
  • 449b176 Add Joe Spisak to Core maintainers list (#172585)
  • f6e6c0a [Graph Partition] Improve support for mutation ops (#172577)
  • 99cb424 Revert "[CI] Add IoU-based accuracy checking for inductor tests segmentation ...
  • 1f74c10 [CI] Add IoU-based accuracy checking for inductor tests segmentation models (...
  • e43b5bf Bump fbgemm and torchrec pinned commit (#172179)
  • 2c9af43 Skip modded_nanogpt model in TorchInductor benchmark (#172141)
  • 0e2459f A few weights_only unpickler fixes (#172105)
  • a266b60 Touch __init__.py in vendored_templates for CuTeDSL Grouped MM template (...
  • f3b5d8b [MPS] Remove error-checking sync point from MaxUnpool (#172111)
  • 3a5fb54 Fix MPS mul performance regression (#172106)
  • Additional commits viewable in compare view

Updates torchvision to 0.25.0

Release notes

Sourced from torchvision's releases.

TorchVision 0.25 Release

TorchVision 0.25 is out! It is compatible with torch 2.10. It's a small release that comes with the following improvements:

Enhancement

[transforms] KeyPoints aren't clamped by default anymore after a transform. This is a bug-fix that comes with a change of behavior. We also added the SanitizeKeyPoints transform to remove keypoints outside of the image area (#9236, #9235) [utils] draw_bounding_boxes now supports a label_background_colors parameter (#9204) [io] Fixed an issue in the GIF decoder (decode_gif, decode_image) which affected some (not all) animated GIFs. (#9241) [misc] Various code-quality and docs improvements (#9218, #9270, #9250, #9247)

Contributors

🎉 We're grateful for our community, which helps us improve Torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release:

Andrei Moraru, Andrey Talman, Antoine Simoulin , Arun Prakash A, Björn Barz, Huy Do, Nicolas Hug, Sean Gilligan, Wes Castro, Zhitao Yu

Commits

Updates matplotlib to 3.10.8

Release notes

Sourced from matplotlib's releases.

REL: v3.10.8

This is a bugfix release in the 3.10.x series.

The primary highlights of this release are:

  • Properly allow freethreaded mode in the MacOS backend
  • Better error handling for MacOS backend
Commits
  • 1392cbe REL: v3.10.8
  • 0b9ebb3 Doc release prep v3.10.8
  • bc7b5c4 Merge branch 'v3.10.7-doc' into v3.10.x
  • 86b38d3 Github stats v3.10.8
  • 9512188 Merge pull request #30717 from meeseeksmachine/auto-backport-of-pr-30714-on-v...
  • d300769 Backport PR #30714: FIX: Gracefully handle numpy arrays as input to check_in_...
  • 799bc95 Merge pull request #30711 from ngoldbaum/v3.10.x
  • 134000b Merge pull request #30697 from ngoldbaum/fix-plotting-on-worker-threads
  • 5b8e219 TST: Run macosx backends in a subprocess
  • 878e71a Backport PR #29810: Declare free-threaded support in MacOS backend extension ...
  • Additional commits viewable in compare view

Updates pandas to 3.0.1

Release notes

Sourced from pandas's releases.

pandas 3.0.1

We are pleased to announce the release of pandas 3.0.1. This is a patch release in the 3.0.x series and includes some regression fixes and bug fixes. We recommend that all users of the 3.0.x series upgrade to this version.

See the full whatsnew for a list of all the changes.

Pandas 3.0.0 supports Python 3.11 and higher. The release can be installed from PyPI:

python -m pip install --upgrade pandas==3.0.*

Or from conda-forge

conda install -c conda-forge pandas=3.0

Please report any issues with the release on the pandas issue tracker.

Thanks to all the contributors who made this release possible.

Commits
  • e04b26f RLS: 3.0.1 (#64206)
  • 47909e6 [backport 3.0.x] ENH: Add item() method to ExtensionArray class (#64134) (#64...
  • a061bfd Backport PR #64199 on branch 3.0.x (DOC: cleanup 3.0.1 whatsnew) (#64201)
  • 085a385 [backport 3.0.x] BUG: Fix read_hdf failing on generic datetime64 dtype (#6400...
  • 5f17047 [backport 3.0.x] BUG: use fill_null fallback for bug in pyarrow 21 on Windows...
  • 0d3a8cb Backport PR #64122 on branch 3.0.x (REG: Allow RE2 syntax in str.contains and...
  • 78e1917 Backport PR #64185 on branch 3.0.x (TST: remove fixed xfail for PyArrow 23.0....
  • 75a42ca Backport PR #64168 on branch 3.0.x (TST: add legacy file generation and tests...
  • 46d443f Backport PR #64092 on branch 3.0.x (BUG: DataFrame.loc fills b'' instead of N...
  • 9d67932 Backport PR #64068 on branch 3.0.x (BUG: fixed to_timedelta with list of int ...
  • Additional commits viewable in compare view

Updates scikit-learn to 1.8.0

Release notes

Sourced from scikit-learn's releases.

Release 1.8.0

We're happy to announce the 1.8.0 release.

You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_8_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.8.html

This version supports Python versions 3.11 to 3.14 and features support of free-threaded CPython.

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn
Commits
  • 646da0f [cd build]
  • 4f4f283 Generate changelog
  • 967dcde Set version
  • cb1424b DOC Release highlights for 1.8 (#32809)
  • 5645b27 🔒 🤖 CI Update lock files for main CI build(s) 🔒 🤖 (#32859)
  • 6b9fb11 🔒 🤖 CI Update lock files for free-threaded CI build(s) 🔒 :rob...
  • a0f6d88 🔒 🤖 CI Update lock files for array-api CI build(s) 🔒 🤖 ...
  • c1de8fc FIX Make get_namespace handle pandas dataframe input (#32838)
  • 764249a Fix _safe_indexing with non integer arrays on array API inputs (#32840)
  • eca5e0a FIX Add new default max_samples=None in Bagging estimators (#32825)
  • Additional commits viewable in compare view

Updates xgboost to 3.2.0

Release notes

Sourced from xgboost's releases.

Release 3.2.0 stable

Release note

https://xgboost.readthedocs.io/en/latest/changes/v3.2.0.html

Additional artifacts

You can verify the downloaded packages by running the following command on your Unix shell:

echo "<hash> <artifact>" | shasum -a 256 --check
16a31dfbc0c54544c9c36ab5f696fa7b646c125f161c52c814d757a58241a404  xgboost-src-3.2.0.tar.gz
41ce6798ed032380d4efed08cb1e4fadb87a5eba401b530fefcb90f1deb367d0  xgboost_r_gpu_linux.tar.gz

Experimental binary packages for R with CUDA enabled

  • xgboost_r_gpu_linux_3.2.0.tar.gz: Download

Source tarball

Changelog

Sourced from xgboost's changelog.

XGBoost Change Log

Starting from 2.1.0, release note is recorded in the documentation.

This file records the changes in xgboost library in reverse chronological order.

2.0.0 (2023 Aug 16)

We are excited to announce the release of XGBoost 2.0. This note will begin by covering some overall changes and then highlight specific updates to the package.

Initial work on multi-target trees with vector-leaf outputs

We have been working on vector-leaf tree models for multi-target regression, multi-label classification, and multi-class classification in version 2.0. Previously, XGBoost would build a separate model for each target. However, with this new feature that's still being developed, XGBoost can build one tree for all targets. The feature has multiple benefits and trade-offs compared to the existing approach. It can help prevent overfitting, produce smaller models, and build trees that consider the correlation between targets. In addition, users can combine vector leaf and scalar leaf trees during a training session using a callback. Please note that the feature is still a working in progress, and many parts are not yet available. See #9043 for the current status. Related PRs: (#8538, #8697, #8902, #8884, #8895, #8898, #8612, #8652, #8698, #8908, #8928, #8968, #8616, #8922, #8890, #8872, #8889, #9509) Please note that, only the hist (default) tree method on CPU can be used for building vector leaf trees at the moment.

New device parameter.

A new device parameter is set to replace the existing gpu_id, gpu_hist, gpu_predictor, cpu_predictor, gpu_coord_descent, and the PySpark specific parameter use_gpu. Onward, users need only the device parameter to select which device to run along with the ordinal of the device. For more information, please see our document page (https://xgboost.readthedocs.io/en/stable/parameter.html#general-parameters) . For example, with device="cuda", tree_method="hist", XGBoost will run the hist tree method on GPU. (#9363, #8528, #8604, #9354, #9274, #9243, #8896, #9129, #9362, #9402, #9385, #9398, #9390, #9386, #9412, #9507, #9536). The old behavior of gpu_hist is preserved but deprecated. In addition, the predictor parameter is removed.

hist is now the default tree method

Starting from 2.0, the hist tree method will be the default. In previous versions, XGBoost chooses approx or exact depending on the input data and training environment. The new default can help XGBoost train models more efficiently and consistently. (#9320, #9353)

GPU-based approx tree method

There's initial support for using the approx tree method on GPU. The performance of the approx is not yet well optimized but is feature complete except for the JVM packages. It can be accessed through the use of the parameter combination device="cuda", tree_method="approx". (#9414, #9399, #9478). Please note that the Scala-based Spark interface is not yet supported.

Optimize and bound the size of the histogram on CPU, to control memory footprint

XGBoost has a new parameter max_cached_hist_node for users to limit the CPU cache size for histograms. It can help prevent XGBoost from caching histograms too aggressively. Without the cache, performance is likely to decrease. However, the size of the cache grows exponentially with the depth of the tree. The limit can be crucial when growing deep trees. In most cases, users need not configure this parameter as it does not affect the model's accuracy. (#9455, #9441, #9440, #9427, #9400).

Along with the cache limit, XGBoost also reduces the memory usage of the hist and approx tree method on distributed systems by cutting the size of the cache by half. (#9433)

Improved external memory support

There is some exciting development around external memory support in XGBoost. It's still an experimental feature, but the performance has been significantly improved with the default hist tree method. We replaced the old file IO logic with memory map. In addition to performance, we have reduced CPU memory usage and added extensive documentation. Beginning from 2.0.0, we encourage users to try it with the hist tree method when the memory saving by QuantileDMatrix is not sufficient. (#9361, #9317, #9282, #9315, #8457)

Learning to rank

We created a brand-new implementation for the learning-to-rank task. With the latest version, XGBoost gained a set of new features for ranking task including:

  • A new parameter lambdarank_pair_method for choosing the pair construction strategy.
  • A new parameter lambdarank_num_pair_per_sample for controlling the number of samples for each group.
  • An experimental implementation of unbiased learning-to-rank, which can be accessed using the lambdarank_unbiased parameter.
  • Support for custom gain function with NDCG using the ndcg_exp_gain parameter.
  • Deterministic GPU computation for all objectives and metrics.
  • NDCG is now the default objective function.
  • Improved performance of metrics using caches.
  • Support scikit-learn utilities for XGBRanker.
  • Extensive documentation on how learning-to-rank works with XGBoost.

For more information, please see the tutorial. Related PRs: (#8771, #8692, #8783, #8789, #8790, #8859, #8887, #8893, #8906, #8931, #9075, #9015, #9381, #9336, #8822, #9222, #8984, #8785, #8786, #8768)

Automatically estimated intercept

... (truncated)

Commits

Updates torch from 2.9.0 to 2.10.0

Release notes

Sourced from torch's releases.

PyTorch 2.10.0 Release Notes

Highlights

For more details about these highlighted features, you can look at the release blogpost. Below are the full release notes for this release.

Backwards Incompatible Changes

Dataloader Frontend

  • Removed unused data_source argument from Sampler (#163134). This is a no-op, unless you have a custom sampler that uses this argument. Please update your custom sampler accordingly.
  • Removed deprecated imports for torch.utils.data.datapipes.iter.grouping (#163438). from torch.utils.data.datapipes.iter.grouping import SHARDING_PRIORITIES, ShardingFilterIterDataPipe is no longer supported. Please import from torch.utils.data.datapipes.iter.sharding instead.

torch.nn

  • Remove Nested Jagged Tensor support from nn.attention.flex_attention (#161734)

... (truncated)

Changelog

Sourced from torch's changelog.

Releasing PyTorch

Release Compatibility Matrix

Following is the Release Compatibility Matrix for PyTorch releases:

... (truncated)

Commits
  • 449b176 Add Joe Spisak to Core maintainers list (#172585)
  • f6e6c0a [Graph Partition] Improve support for mutation ops (#172577)
  • 99cb424 Revert "[CI] Add IoU-based accuracy checking for inductor tests segmentation ...
  • 1f74c10 [CI] Add IoU-based accuracy checking for inductor tests segmentation models (...
  • e43b5bf Bump fbgemm and torchrec pinned commit (#172179)
  • 2c9af43 Skip modded_nanogpt model in TorchInductor benchmark (#172141)
  • 0e2459f A few weights_only unpickler fixes (#172105)
  • a266b60 Touch __init__.py in vendored_templates for CuTeDSL Grouped MM template (...
  • f3b5d8b [MPS] Remove error-checking sync point from MaxUnpool (#172111)
  • 3a5fb54 Fix MPS mul performance regression (#172106)
  • Additional commits viewable in compare view

Updates matplotlib from 3.10.7 to 3.10.8

Release notes

Sourced from matplotlib's releases.

REL: v3.10.8

This is a bugfix release in the 3.10.x series.

The primary highlights of this release are:

  • Properly allow freethreaded mode in the MacOS backend
  • Better error handling for MacOS backend
Commits
  • 1392cbe REL: v3.10.8
  • 0b9ebb3 Doc release prep v3.10.8
  • bc7b5c4 Merge branch 'v3.10.7-doc' into v3.10.x
  • 86b38d3 Github stats v3.10.8
  • 9512188 Merge pull request #30717 from meeseeksmachine/auto-backport-of-pr-30714-on-v...
  • d300769 Backport PR #30714: FIX: Gracefully handle numpy arrays as input to check_in_...
  • 799bc95 Merge pull request #30711 from ngoldbaum/v3.10.x
  • 134000b Merge pull request #30697 from ngoldbaum/fix-plotting-on-worker-threads
  • 5b8e219 TST: Run macosx backends in a subprocess
  • 878e71a Backport PR #29810: Declare free-threaded support in MacOS backend extension ...
  • Additional commits viewable in compare view

Updates pandas from 2.3.3 to 3.0.1

Release notes

Sourced from pandas's releases.

pandas 3.0.1

We are pleased to announce the release of pandas 3.0.1. This is a patch release in the 3.0.x series and includes some regression fixes and bug fixes. We recommend that all users of the 3.0.x series upgrade to this version.

See the full whatsnew for a list of all the changes.

Pandas 3.0.0 supports Python 3.11 and higher. The release can be installed from PyPI:

python -m pip install --upgrade pandas==3.0.*

Or from conda-forge

conda install -c conda-forge pandas=3.0

Please report any issues with the release on the pandas issue tracker.

Thanks to all the contributors who made this release possible.

Commits
  • e04b26f RLS: 3.0.1 (#64206)
  • 47909e6 [backport 3.0.x] ENH: Add item() method to ExtensionArray class (#64134) (#64...
  • a061bfd Backport PR #64199 on branch 3.0.x (DOC: cleanup 3.0.1 whatsnew) (#64201)
  • 085a385 [backport 3.0.x] BUG: Fix read_hdf failing on generic datetime64 dtype (#6400...
  • 5f17047 [backport 3.0.x] BUG: use fill_null fallback for bug in pyarrow 21 on Windows...
  • 0d3a8cb Backport PR #64122 on branch 3.0.x (REG: Allow RE2 syntax in str.contains and...
  • 78e1917 Backport PR #64185 on branch 3.0.x (TST: remove fixed xfail for PyArrow 23.0....
  • 75a42ca Backport PR #64168 on branch 3.0.x (TST: add legacy file generation and tests...
  • 46d443f Backport PR #64092 on branch 3.0.x (BUG: DataFrame.loc fills b'' instead of N...
  • 9d67932 Backport PR #64068 on branch 3.0.x (BUG: fixed to_timedelta with list of int ...
  • Additional commits viewable in compare view

Updates scikit-learn from 1.7.2 to 1.8.0

Release notes

Sourced from scikit-learn's releases.

Release 1.8.0

We're happy to announce the 1.8.0 release.

You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_8_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.8.html

This version supports Python versions 3.11 to 3.14 and features support of free-threaded CPython.

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn
Commits
  • 646da0f [cd build]
  • 4f4f283 Generate changelog
  • 967dcde Set version
  • cb1424b DOC Release highlights for 1.8 (#32809)
  • 5645b27 🔒 🤖 CI Update lock files for main CI build(s) 🔒 🤖 (#32859)
  • 6b9fb11 🔒 🤖 CI Update lock files for free-threaded CI build(s) 🔒 :rob...
  • a0f6d88 🔒 🤖 CI Update lock files for array-api CI build(s) 🔒 🤖 ...
  • c1de8fc FIX Make get_namespace handle pandas dataframe input (#32838)
  • 764249a Fix _safe_indexing with non integer arrays on array API inputs (#32840)
  • eca5e0a FIX Add new default max_samples=None in Bagging estimators (#32825)
  • Additional commits viewable in compare view

Updates numpy from 2.3.4 to 2.4.2

Release notes

Sourced from numpy's releases.

2.4.2 (Feb 1, 2026)

NumPy 2.4.2 Release Notes

The NumPy 2.4.2 is a patch release that fixes bugs discovered after the 2.4.1 release. Highlights are:

  • Fixes memory leaks
  • Updates OpenBLAS to fix hangs

This release supports Python versions 3.11-3.14

Contributors

A total of 9 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

  • Charles Harris
  • Daniel Tang +
  • Joren Hammudoglu
  • Kumar Aditya
  • Matti Picus
  • Nathan Goldbaum
  • Ralf Gommers
  • Sebastian Berg
  • Vikram Kumar +

Pull requests merged

A total of 12 pull requests were merged for this release.

  • #30629: MAINT: Prepare 2.4.x for further development
  • #30636: TYP: arange: accept datetime strings
  • #30657: MAINT: avoid possible race condition by not touching os.environ...
  • #30700: BUG: validate contraction axes in tensordot (#30521)
  • #30701: DOC: __array_namespace__info__: set_module not __module__ (#30679)
  • #30702: BUG: fix free-threaded PyObject layout in replace_scalar_type_names...
  • #30703: TST: fix limited API example in tests for latest Cython
  • #30709: BUG: Fix some bugs found via valgrind (#30680)
  • #30712: MAINT: replace ob_type access with Py_TYPE in PyArr...

    Description has been truncated

@dependabot
chore(deps): bump the py-dependencies group across 3 directories with…
18c51d1 
… 8 updates

Updates the requirements on [torch](https://github.com/pytorch/pytorch), [torchvision](https://github.com/pytorch/vision), [matplotlib](https://github.com/matplotlib/matplotlib), [pandas](https://github.com/pandas-dev/pandas), [scikit-learn](https://github.com/scikit-learn/scikit-learn), [xgboost](https://github.com/dmlc/xgboost), [numpy](https://github.com/numpy/numpy) and [joblib](https://github.com/joblib/joblib) to permit the latest version.

Updates `torch` to 2.10.0
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v2.9.0...v2.10.0)

Updates `torchvision` to 0.25.0
- [Release notes](https://github.com/pytorch/vision/releases)
- [Commits](pytorch/vision@v0.24.0...v0.25.0)

Updates `matplotlib` to 3.10.8
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](matplotlib/matplotlib@v3.10.7...v3.10.8)

Updates `pandas` to 3.0.1
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Commits](pandas-dev/pandas@v2.3.3...v3.0.1)

Updates `scikit-learn` to 1.8.0
- [Release notes](https://github.com/scikit-learn/scikit-learn/releases)
- [Commits](scikit-learn/scikit-learn@1.7.2...1.8.0)

Updates `xgboost` to 3.2.0
- [Release notes](https://github.com/dmlc/xgboost/releases)
- [Changelog](https://github.com/dmlc/xgboost/blob/master/NEWS.md)
- [Commits](dmlc/xgboost@v3.1.0...v3.2.0)

Updates `torch` from 2.9.0 to 2.10.0
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v2.9.0...v2.10.0)

Updates `matplotlib` from 3.10.7 to 3.10.8
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](matplotlib/matplotlib@v3.10.7...v3.10.8)

Updates `pandas` from 2.3.3 to 3.0.1
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Commits](pandas-dev/pandas@v2.3.3...v3.0.1)

Updates `scikit-learn` from 1.7.2 to 1.8.0
- [Release notes](https://github.com/scikit-learn/scikit-learn/releases)
- [Commits](scikit-learn/scikit-learn@1.7.2...1.8.0)

Updates `numpy` from 2.3.4 to 2.4.2
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](numpy/numpy@v2.3.4...v2.4.2)

Updates `joblib` from 1.5.2 to 1.5.3
- [Release notes](https://github.com/joblib/joblib/releases)
- [Changelog](https://github.com/joblib/joblib/blob/main/CHANGES.rst)
- [Commits](joblib/joblib@1.5.2...1.5.3)

---
updated-dependencies:
- dependency-name: torch
  dependency-version: 2.10.0
  dependency-type: direct:production
  dependency-group: py-dependencies
- dependency-name: torchvision
  dependency-version: 0.25.0
  dependency-type: direct:production
  dependency-group: py-dependencies
- dependency-name: matplotlib
  dependency-version: 3.10.8
  dependency-type: direct:production
  dependency-group: py-dependencies
- dependency-name: pandas
  dependency-version: 3.0.1
  dependency-type: direct:production
  dependency-group: py-dependencies
- dependency-name: scikit-learn
  dependency-version: 1.8.0
  dependency-type: direct:production
  dependency-group: py-dependencies
- dependency-name: xgboost
  dependency-version: 3.2.0
  dependency-type: direct:production
  dependency-group: py-dependencies
- dependency-name: torch
  dependency-version: 2.10.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: py-dependencies
- dependency-name: matplotlib
  dependency-version: 3.10.8
  dependency-type: direct:production
  update-type: version-update:semver-patch
  dependency-group: py-dependencies
- dependency-name: pandas
  dependency-version: 3.0.1
  dependency-type: direct:production
  update-type: version-update:semver-major
  dependency-group: py-dependencies
- dependency-name: scikit-learn
  dependency-version: 1.8.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: py-dependencies
- dependency-name: numpy
  dependency-version: 2.4.2
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: py-dependencies
- dependency-name: joblib
  dependency-version: 1.5.3
  dependency-type: direct:production
  update-type: version-update:semver-patch
  dependency-group: py-dependencies
...

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@dependabot dependabot bot added dependencies Pull requests that update a dependency file python Pull requests that update Python code labels Mar 1, 2026
@dependabot dependabot bot mentioned this pull request Mar 1, 2026
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