[TST] Add test runners for Python 3.11-3.13

[alyssadai]

• Closes Add test runners for Python 3.11-3.13 #546

• Note: the README already had Python 3.10-3.13 specified as supported

Checklist

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• Tests pass
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• Tests have been added

For bug fixes:

• There is at least one test that would fail under the original bug conditions.

Summary by Sourcery

Test the project across all supported Python versions and maintain compatibility with pandas below version 3.

Enhancements:

• Expand the test workflow to run against Python 3.10 through 3.13.
• Constrain the pandas dependency to versions below 3 for compatibility.

CI:

• Run the test suite in parallel across the supported Python versions without stopping other matrix jobs after a failure.

Tests:

• Add CI coverage for Python 3.11, 3.12, and 3.13.

[sourcery-ai]

Reviewer's guide (collapsed on small PRs)

Reviewer's Guide

The test workflow now runs the existing test job independently on Python 3.10, 3.11, 3.12, and 3.13, with uv configured from the matrix and failures isolated across versions.

File-Level Changes

Change	Details	Files
Expand the CI test workflow into a multi-version Python test matrix.	Add Python 3.10–3.13 versions to the job matrix. Disable fail-fast so every Python version completes independently. Parameterize uv setup with the matrix-selected Python version.	.github/workflows/test.yaml

Assessment against linked issues

Issue	Objective	Addressed	Explanation
#546	Add CI test runners for Python 3.11, 3.12, and 3.13 while retaining testing on Python 3.10.	✅

Possibly linked issues

• Add test runners for Python 3.11-3.13 #546: The PR directly implements the issue by adding Python 3.11-3.13 versions to the CI test matrix.

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 95.20%. Comparing base (9971f37) to head (0e2be8b).

Additional details and impacted files

@@           Coverage Diff           @@
##             main     #598   +/-   ##
=======================================
  Coverage   95.20%   95.20%           
=======================================
  Files          29       29           
  Lines        1355     1355           
  Branches       90       90           
=======================================
  Hits         1290     1290           
  Misses         36       36           
  Partials       29       29           

☔ View full report in Codecov by Harness.
📢 Have feedback on the report? Share it here.

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• ❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.

[surchs]

Thanks @alyssadai. The fix looks good, but everything beyond 3.10 seems to fail. The bot thinks this section here is the culprit:

api/app/api/utility.py

Lines 428 to 437 in 9971f37

	# TODO: Revisit this as there may be a more elegant solution.
	# The following code replaces columns with all NaN values with values of None, to ensure they show up in the final JSON as `null`.
	# This is needed as the above .agg() seems to turn NaN into None for object-type columns (which have some non-missing values)
	# but not for columns with all NaN, which end up with a column type of float64. This is a problem because
	# if the column corresponds to a SessionResponse attribute with an expected str type, then the column values will be converted
	# to the string "nan" in the response JSON, which we don't want.
	all_nan_columns = subject_data.columns[subject_data.isna().all()]
	subject_data[all_nan_columns] = subject_data[all_nan_columns].replace(
	{np.nan: None}
	)

I didn't look into it more closely than that. But I'd say we fix it in this PR.

[alyssadai]

Thanks @surchs for flagging that. I think this is due to the new default type inference behaviour in pandas>=3 (which is used by default for Python>=3.11). Before pandas 3, string columns with some none-type values would be typed as object, but with pandas 3+, they are typed as str, with missing values being converted to np.nan. This breaks the type checking for some our current Pydantic models. I think a long-term fix would be to normalize missing values to None to ensure they are not interpreted as numeric, but for now I have pinned the pandas version.

• tracking issue: Ensure empty data in query response str fields work with pandas>=3 #599
• also related to Test with empty BIDS table fails on pandas>=3 bagel-cli#671

[surchs]

Got it, thanks! 🧑‍🍳

[neurobagel-bot]

🚀 PR was released in v0.11.0 🚀