Mailman 3 March 2026 - NumPy-Discussion

next NumPy triage meeting - Wednesday, February 18th, 2026 at 18:00 UTC
by Inessa Pawson April 12, 2026

April 12, 2026

Our next triage meeting will be held this Wednesday, February 18th at 18:00 (6 pm) UTC. This is a meeting where we synchronously triage prioritized PRs and issues. Everyone is welcome to attend and contribute to a conversation. Join us via Zoom: https://numfocus-org.zoom.us/j/82096749952?pwd=MW9oUmtKQ1c3a2gydGk1RTdYUUVX… If there are PRs or issues you’d like us to review, please add GitHub links to them to the meeting agenda: https://hackmd.io/68i_JvOYQfy9ERiHgXMPvg -- Cheers, Inessa Inessa Pawson GitHub: inessapawson

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next NumPy community meeting - Wednesday, April 9th, 2025 at 5 pm UTC - now meeting ONE HOUR EARLIER
by Inessa Pawson April 5, 2026

April 5, 2026

The next NumPy community meeting will be held this Wednesday, April 9th at 17:00 (5 pm) UTC. Please note the TIME CHANGE! Join us via Zoom: https://numfocus-org.zoom.us/j/83278611437?pwd=ekhoLzlHRjdWc0NOY2FQM0NPemdk… Everyone is welcome and encouraged to attend. To add to the meeting agenda the topics you’d like to discuss, follow the link: https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg?both For the notes from the previous meetings, visit: https://github.com/numpy/archive/tree/main/community_meetings -- Cheers, Inessa Inessa Pawson GitHub: inessapawson

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next NumPy Optimization Team meeting - Monday, January 20th, 2025 at 5 pm UTC
by Inessa Pawson April 3, 2026

April 3, 2026

Hi, all! The next NumPy Optimization Team meeting will be held on Monday, January 20th at 17:00 UTC. Join us via Zoom: https://numfocus-org.zoom.us/j/81261288210?pwd=iwV99tGSjR61RTGEERKM4QKxe46g… Everyone is welcome and encouraged to attend. To add to the meeting agenda the topics you’d like to discuss, follow the link: https://hackmd.io/dVdSlQ0TThWkOk0OkmGsmw?both For the notes from the previous meetings, visit: https://github.com/numpy/archive/tree/main/optim_team_meetings -- Cheers, Inessa Inessa Pawson GitHub: inessapawson

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Deprecating unitless timedelta/datetime
by Sebastian Berg March 29, 2026

March 29, 2026

Hi all, I just merged a PR: https://github.com/numpy/numpy/pull/29619 to deprecate unitless timdeldta/datetime64. This is a step in a direction that should be e.g. very helpful for pandas to make timedelta/datetimes less weird. That said, we didn't touch this for a long time, because it is also very hard to really understand how and if users might be impacted. So if you notice this, or maybe even think you may be mixing timedeltas with normal integers/floats a lot you may want to try this branch. This is a `DeprecationWarning`, so it shouldn't out-right break code in production. Note that there are also specific things we could try to make more gentle, even something niche like specifically allowing: `timdelta_arr == 0` (although `timedelta_arr.astype(bool)` should be a plausible replacement as well, I think). Or generally keep allowing `timedelta_arr + integer_arr`, etc. which, with this PR, I believe should give a warning. Cheers, Sebastian

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NumPy 2.4.4 Released
by Charles R Harris March 29, 2026

March 29, 2026

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 2.4.4. NumPy 2.4.4 is a patch release that fixes bugs discovered after the 2.4.3 release. It should finally close issue #30816, the OpenBLAS threading problem on ARM. This release supports Python versions 3.11-3.14. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/2.4.4>; release notes and the changelog are available on Github <https://github.com/numpy/numpy/releases/tag/v2.4.4>. *Contributors* A total of 8 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time. - Charles Harris - Daniel Haag + - Denis Prokopenko + - Harshith J + - Koki Watanabe - Marten van Kerkwijk - Matti Picus - Nathan Goldbaum *Pull requests merged* A total of 7 pull requests were merged for this release. - #30978: MAINT: Prepare 2.4.x for further development - #31049: BUG: Add test to reproduce problem described in #30816 (#30818) - #31052: BUG: fix FNV-1a 64-bit selection by using NPY_SIZEOF_UINTP (#31035) - #31053: BUG: avoid warning on ufunc with where=True and no output - #31058: DOC: document caveats of ndarray.resize on 3.14 and newer - #31079: TST: fix POWER VSX feature mapping (#30801) - #31084: MAINT: numpy.i: Replace deprecated `sprintf` with `snprintf`... Cheers, Charles Harris

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Localization of The NumPy Documentation
by Haowei Hsu March 21, 2026

March 21, 2026

Hello, NumPy Community, I am the author of the Localize The Docs organization on GitHub: https://github.com/localizethedocs And I’m glad to announce that the 🎉 numpy-docs-l10n 🎉 project is published now: 🚀 Preview: https://projects.localizethedocs.org/numpy-docs-l10n 🌐 Crowdin: https://localizethedocs.crowdin.com/numpy-docs-l10n 🐙 GitHub: https://github.com/localizethedocs/numpy-docs-l10n The goal of this project is to translate The NumPy Documentation into multiple languages. Translations are contributed via the Crowdin platform, automatically synchronized with the GitHub repository, and can be previewed on GitHub Pages. We welcome anyone interested in documentation translation to join us.

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NumPy Triage Team permissions review
by Inessa Pawson March 17, 2026

March 17, 2026

Hi, all! The NumPy Steering Council is currently reviewing permissions across all project repositories. As part of this process, we will be removing triage rights from members of the Triage Team who have not performed triage activities in the past 12+ months. If your access is removed but you would like to resume contributing to triage, please reach out to the Steering Council via numpy-team(a)googlegroups.com We will review requests for reinstatement on a case-by-case basis. -- Cheers, Inessa Inessa Pawson GitHub: inessapawson

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Code Coverage for C and Python
by Ganesh Kathiresan March 16, 2026

March 16, 2026

Hi all, I wanted to get a wider feedback on adding a code coverage CI for C and Python. Prototype PR Link: https://github.com/numpy/numpy/pull/30911 *Why add this CI:* - diff-cover provides a way to see coverage only on the changed files, which is good for big PRs to understand the UT compared to the main branch. - With the new GH actions-upload (v7), we can get a clickable link to the report in a single file. For now the PR implements a MD file format as HTML file leads to ~250MB file *Why not to add this CI:* - Today code coverage and dead code can be calculated locally by following this: https://numpy.org/doc/stable/dev/index.html#test-coverage. So there is not much value in running this for every PR Please let us know your thoughts on having this new CI action. Thanks, Ganesh (https://github.com/ganesh-k13)

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Should we revert float16 loops (and what are the precision requirements)?
by Sebastian Berg March 11, 2026

March 11, 2026

Hi all, In the NumPy 2.4 cycle, there were some native float16 implementations merged with rather low precision leading to the following issue: https://github.com/numpy/numpy/issues/30821 That is, previously, it used float loops so ~0.5 ULP error, now is is 2+ULP for many algorithms, on _some_ hardware: https://github.com/numpy/numpy/pull/23351 There is always an argument around that users of float16 probably don't care about many ULP, but I guess they also have very few bits of precision to begin with? I don't have a huge opinion on it, but we are more and more in the position where it is unclear if sacrificing a bit of precision is the right thing or not... Similar questions actually arise for float32 math, is it OK to trade- off precision for performance (or to what degree, everything trades a bit)? We have had discussions around this before but it is still a difficult trade-off to make and there is no choice that makes everyone happy. [1] - Sebastian [1] We can work towards something like `np.opts(precision="low")` or so, but that doesn't change the question of defaults much...

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NumPy 2.4.3 Release
by Charles R Harris March 9, 2026

March 9, 2026

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 2.4.3. The NumPy 2.4.3 is a patch release that fixes bugs discovered after the 2.4.2 release. The most user visible fix may be a threading fix for OpenBLAS on ARM, closing issue #30816. This release supports Python versions 3.11-3.14. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/2.4.3>; release notes and the changelog are available on Github <https://github.com/numpy/numpy/releases/tag/v2.4.3>. *Contributors* A total of 11 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Antareep Sarkar + - Charles Harris - Joren Hammudoglu - Matthieu Darbois - Matti Picus - Nathan Goldbaum - Peter Hawkins - Pieter Eendebak - Sebastian Berg - Warren Weckesser - stratakis + *Pull requests merged* A total of 14 pull requests were merged for this release. - #30759: MAINT: Prepare 2.4.x for further development - #30827: BUG: Fix some leaks found via LeakSanitizer (#30756) - #30841: MAINT: Synchronize 2.4.x submodules with main - #30849: TYP: matlib: missing extended precision imports - #30850: BUG: Fix weak hash function in np.isin(). (#30840) - #30921: BUG: fix infinite recursion in np.ma.flatten_structured_array... - #30922: BUG: Fix buffer overrun in CPU baseline validation (#30877) - #30923: BUG: Fix busdaycalendar's handling of a bool array weekmask... - #30924: BUG: Fix reference leaks and NULL pointer dereferences (#30908) - #30925: MAINT: fix two minor issues noticed when touching the C API setup - #30955: ENH: Test .kind not .char in np.testing.assert_equal (#30879) - #30957: BUG: fix type issues in uses if PyDataType macros - #30958: MAINT: Don't use vulture 2.15, it has false positives - #30973: MAINT: update openblas (#30961) Cheers, Charles Harris

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