Bump numpy from 2.2.6 to 2.5.3

[dependabot]

Bumps numpy from 2.2.6 to 2.5.3.

Release notes

Sourced from numpy's releases.

v2.5.3 (Sep 6, 2026)

NumPy 2.5.3 Release Notes

The NumPy 2.5.3 is a patch release that fixes bugs discovered after the 2.5.2 release. Apart from the usual bug and maintenance work, there are a number of StringDType related fixes for problems discovered during the ongoing string work in the main branch.

This release supports Python versions 3.12-3.15

Changes

• Casting a fixed-width byte string array (np.bytes_) to StringDType now raises TypeError when the bytes are not valid UTF-8. Previously the invalid bytes were stored as-is and later caused undefined behavior in string operations.

  (gh-32296)

• MaskedArray._fill_value would become stale when ufuncs that change dtype left the result holding a fill_value typed for the old dtype. The mismatch was silent until something later called _check_fill_value, such as .view(), and then a TypeError would be raised. Now, when the copied fill_value is no longer valid for the new dtype, fall back to the default fill_value for that dtype instead of propagating the stale value. This may raise a ComplexWarning if the fill_value is complex and the new dtype is real.

  (gh-32423)

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
• Iason Krommydas
• James Davies +
• Joren Hammudoglu
• Maanas Arora
• Matti Picus
• Nathan Goldbaum
• Shikhar Goel +
• Yeonho Kim +

Pull requests merged

A total of 27 pull requests were merged for this release.

• #32235: MAINT: Prepare 2.5.x for further development

... (truncated)

Changelog

Sourced from numpy's changelog.

This is a walkthrough of the NumPy 2.4.0 release on Linux, which will be the first feature release using the numpy/numpy-release <https://github.com/numpy/numpy-release>__ repository.

The commands can be copied into the command line, but be sure to replace 2.4.0 with the correct version. This should be read together with the :ref:general release guide <prepare_release>.

Facility preparation

Before beginning to make a release, use the requirements/*_requirements.txt files to ensure that you have the needed software. Most software can be installed with pip, but some will require apt-get, dnf, or whatever your system uses for software. You will also need a GitHub personal access token (PAT) to push the documentation. There are a few ways to streamline things:

• Git can be set up to use a keyring to store your GitHub personal access token. Search online for the details.

Prior to release

Add/drop Python versions

When adding or dropping Python versions, multiple config and CI files need to be edited in addition to changing the minimum version in pyproject.toml. Make these changes in an ordinary PR against main and backport if necessary. We currently release wheels for new Python versions after the first Python RC once manylinux and cibuildwheel support that new Python version.

Backport pull requests

Changes that have been marked for this release must be backported to the maintenance/2.4.x branch.

Update 2.4.0 milestones

Look at the issues/prs with 2.4.0 milestones and either push them off to a later version, or maybe remove the milestone. You may need to add a milestone.

Check the numpy-release repo

... (truncated)

Commits

• dd88c0c Merge pull request #32511 from charris/prepare-2.5.3
• edcac6a REL: Prepare for the NumPy 2.5.3 release
• fd4d908 Merge pull request #32509 from charris/backport-32496
• 65bb1da BUG: fix crash in ufunc.resolve_dtypes with a Python scalar type (#32496)
• 294956e Merge pull request #32506 from charris/backport-32503
• 26428d9 DOC: fix scipy docs links in intersphinx mapping (#32507)
• 5fab1cb DOC: use static scipy doc site for intershpinx (#32503)
• 7beed2f Merge pull request #32481 from ngoldbaum/stringdtype-backport
• 8972f70 Merge pull request #32478 from charris/backport-32466
• ab1b589 Merge pull request #32477 from charris/backport-32423
• Additional commits viewable in compare view

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

---

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

• @dependabot rebase will rebase this PR
• @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
• @dependabot show <dependency name> ignore conditions will show all of the ignore conditions of the specified dependency
• @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
• @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
• @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)

[villelaitila]

Reviewed this dependency bump (numpy 2.2.6 → 2.5.3). The diff itself is a single-line version pin change, but it surfaces one real compatibility concern flagged inline: NumPy 2.5.x has dropped support for Python 3.8-3.11, while this project's setup.cfg still declares support down to Python 3.8. Since numpy is a direct import (src/sgraph/cypher.py) and there's no CI matrix testing across the declared Python versions, this would only surface as an install failure for anyone still on Python 3.8-3.11. No other logical issues found in the diff.

---

Generated by Claude Code

[villelaitila]

Potential Python-version compatibility gap introduced by this bump: NumPy 2.5.3's own release notes state "This release supports Python versions 3.12-3.15," i.e. it drops wheels/support for 3.8-3.11. This repo's setup.cfg still declares python_requires = >=3.8 and lists classifiers for Python 3.8-3.13.

numpy is a direct import in src/sgraph/cypher.py (not merely a transitive dependency of pandas), and it's pinned here as a plain (non-optional) dev/test requirement — unlike spycy-aneeshdurg, which is explicitly commented as optional. So pip install -r requirements.txt would fail outright on Python 3.8-3.11, even though the package still advertises support for those versions.

Since there's no CI matrix in this repo running the test suite across the declared Python versions, this gap wouldn't be caught automatically before merge. Worth either verifying nothing here still needs to run on 3.8-3.11, or holding this bump (or pinning an intermediate numpy version) until the supported-version floor is reconciled.

---

Generated by Claude Code