Bump pandas from 2.3.3 to 3.0.5

[dependabot]

Bumps pandas from 2.3.3 to 3.0.5.

Release notes

Sourced from pandas's releases.

pandas 3.0.5

We are pleased to announce the release of pandas 3.0.5. 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 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.

pandas 3.0.4

We are pleased to announce the release of pandas 3.0.4. 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 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.

pandas 3.0.3

We are pleased to announce the release of pandas 3.0.3. 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 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

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Commits

• e68db09 RLS: 3.0.5
• 0697623 Backport PR #66428: DOC: update whatsnew for 3.0.5 (#66430)
• 5d846d0 Backport PR #66090 on branch 3.0.x (BLD: exclude numpy 2.5.0 when building wh...
• bef7a2b Backport PR #66169 on branch 3.0.x (CI: Fix pyarrow-nightly job (stale nightl...
• 8188eb1 RLS: 3.0.4 (#66079)
• bd35f15 [3.0.x] BUG: fix CoW issue in eval() (#66072)
• 6195872 [backport 3.0.x] BUG: anchor whole alternation in Series.str.match for PyArro...
• 70313be Backport PR #66051 on branch 3.0.x (BUG: fix regression in DataFrame setitem ...
• 57ed3e2 [backport 3.0.x] Bump pypa/cibuildwheel from 3.4.1 to 4.1.0 (#65934) (#66026)
• f9693fc [backport 3.0.x] BUG(pandas 3.0 regression): drop(index=...) doesn't accept N...
• Additional commits viewable in compare view

[villelaitila]

Reviewed this dependency bump (pandas 2.3.3 → 3.0.5 in requirements.txt).

Verification done: installed pandas 3.0.5 and ran the full suite (PYTHONPATH=src python -m pytest) — all 566 tests pass. I also manually exercised the two files that actually import pandas (src/sgraph/cypher.py, src/sgraph/attributes/attributequeries.py) against representative TSV/CSV input to probe for regressions from pandas 3.0's biggest breaking changes (copy-on-write on by default, new default string dtype via future.infer_string=True). No breakage found — missing values still surface as float NaN rather than pd.NA in the code paths I tried, so the existing math.isnan(val) check in attributeloader.py:43 and the val is pd.NA checks in cypher.py (lines 272, 376) still behave as before.

One gap worth flagging, not a blocker: this is a major-version jump (skips all of pandas 3.0's breaking changes at once), and attributequeries.py/attributeloader.py — the code path that loads real attribute CSVs (attr_*.csv[.zip]) into the model via read_attrs/read_attrs_generic — has no dedicated unit test at all (confirmed via grep over tests/). It's only pandas-consuming module in the repo without any coverage. That means a subtler dtype/NA regression in production attribute files (e.g. an all-empty or all-numeric-looking string column tripping the new string-dtype inference) wouldn't be caught by this PR's CI run even though it passed. Given the tests currently pass, I'd merge this, but consider following up with a small regression test using a sample attrs file so future pandas bumps here aren't flying blind.

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