Mailman 3 May 2025 - NumPy-Discussion

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

June 5, 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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next NumPy community meeting - Wednesday, April 9th, 2025 at 5 pm UTC - now meeting ONE HOUR EARLIER
by Inessa Pawson May 16, 2026

May 16, 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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Fixing definition of reduceat for Numpy 2.0?
by Martin Ling Feb. 27, 2026

Feb. 27, 2026

Hi folks, I don't follow numpy development in much detail these days but I see that there is a 2.0 release planned soon. Would this be an opportunity to change the behaviour of 'reduceat'? This issue has been open in some form since 2006! https://github.com/numpy/numpy/issues/834 The current behaviour was originally inherited from Numeric, and makes reduceat often unusable in practice, even where it should be the perfect, concise, efficient solution. But it has been impossible to change it without breaking compatibіlity with existing code. As a result, horrible hacks are needed instead, e.g. my answer here: https://stackoverflow.com/questions/57694003 Is this something that could finally be fixed in 2.0? Martin

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next NumPy triage meeting - Wednesday, May 28th, 2025 at 5 pm UTC
by Inessa Pawson Jan. 4, 2026

Jan. 4, 2026

The next NumPy triage meeting will be held this Wednesday, May 28th at 17:00 (5 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… Please notify us of issues or PRs that you’d like to have reviewed by adding a GitHub link to them in the meeting agenda: https://hackmd.io/68i_JvOYQfy9ERiHgXMPvg -- Cheers, Inessa Inessa Pawson GitHub: inessapawson

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next NumPy Newcomers' Hour - June 27th, 2024 at 10 pm UTC
by Inessa Pawson Sept. 12, 2025

Sept. 12, 2025

Our next Newcomers' Hour will be held TOMORROW, June 27th at 10 pm UTC. Stop by to ask questions, share your progress, celebrate success, or just to say hi. To add to the meeting agenda the topics you’d like to discuss, follow the link: https://hackmd.io/3f3otyyuTte3FU9y3QzsLg?both. Join the meeting via Zoom: https://us06web.zoom.us/j/82563808729?pwd=ZFU3Z2dMcXBGb05YemRsaGE1OW5nQT09. -- Cheers, Inessa Inessa Pawson GitHub: inessapawson

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Bumping CPU baseline to x86-64-v2
by Sayed Adel June 15, 2025

June 15, 2025

Hi All, I wanted to bring your attention to an important change to NumPy that will affect CPU compatibility requirements on x86. PR #28896 (https://github.com/numpy/numpy/pull/28896) is reorganizing how NumPy handles x86 CPU features by moving from individual instruction set extensions to standardized microarchitecture levels. As part of this change, *the minimum CPU requirement for x86 systems will be raised to x86-64-v2*If your x86 CPU was manufactured in 2009 or later, you likely won't be affected, unless you're using Intel Atom CPUs released before 2013. We're making this change in 2025, when CPUs that don't support x86-64-v2 are now over 15 years old. Supporting these antiquated processors increases maintenance burden and binary size. Bumping the default baseline can also increase the performance since not all code paths are dispatched. -- Cheers, Sayed Sayed Adel GitHub: seiko2plus

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Interesting discussion of ``fast-math``
by Charles R Harris May 30, 2025

May 30, 2025

Hi All, Thought some might find this discussion <https://simonbyrne.github.io/notes/fastmath/> interesting. Chuck

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NumPy 2.3.0rc1 Released
by Charles R Harris May 25, 2025

May 25, 2025

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 2.3.0rc1. The NumPy 2.3.0rc1 release continues the work to improve free threaded Python support and annotations together with the usual set of bug fixes. It is unusual in the number of expired deprecations and the number of code modernizations and style cleanups. The latter may not be visible to users, but is important for code maintenance over the long term. Note that we have also upgraded from manylinux2014 to manylinux_2_28. There are known test failures in the rc1 release involving MyPy and PyPy. The cause of both has been determined and fixes will be applied before the final release. The current Windows on ARM wheels also lack OpenBLAS, but they should suffice for initial downstream testing. OpenBLAS will be incorporated in those wheels when it becomes available. Highlights for this release are: - Interactive examples in the NumPy documentation. - Building NumPy with OpenMP Parallelization. - Preliminary support for Windows on ARM. - Improved support for free threaded Python. - Improved annotations. This release supports Python 3.10-3.13. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/2.3.0rc1>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v2.3.0rc1>. Cheers, Charles Harris

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ANN: Python-Blosc2 3.3.4 released
by Francesc Alted May 22, 2025

May 22, 2025

Hi, This is a bugfix release, with some minor optimizations. We further improved the correct chaining of *string* lazy expressions (to allow operands with more diverse data types). In addition, both indexing and where expressions are now supported within *string* lazy expressions. Finally, casting rules have been improved to be more consistent with NumPy. You can think of Python-Blosc2 3.x as an extension of NumPy/numexpr that: - Can deal with ndarrays compressed using first-class codecs & filters. - Performs many kind of math expressions, including reductions, indexing... - Supports broadcasting operations. - Supports NumPy ufunc mechanism: mix and match NumPy and Blosc2 computations. - Integrates with Numba and Cython via UDFs (User Defined Functions). - Adheres to modern NumPy casting rules way better than numexpr. - Supports linear algebra operations (like ``blosc2.matmul()``). Install it with:: pip install blosc2 --update # if you prefer wheels conda install -c conda-forge python-blosc2 mkl # if you prefer conda and MKL For more info, you can have a look at the release notes in: https://github.com/Blosc/python-blosc2/releases Have fun! -- Francesc Alted

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NumPy 2.3.x has been branched
by Charles R Harris May 21, 2025

May 21, 2025

Hi All, The main branch is now open for NumPy 2.4.0 development. Chuck

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