On Tue, May 24, 2022 at 3:24 PM Ewout ter Hoeven < E.M.terHoeven@student.tudelft.nl> wrote:
Personally I would be in favor of updating NEP 29 to a support timespan in which at most 3 (minor) Python versions are supported. The development of Python is still at a high pace and NumPy is a high performance library which thrives when be able to adopt the latest Python features and having clean codebase without having "if sys.version_info[:2] < (3, n):" guards everywhere.
Especially with the developments of the faster Faster CPython project and the continued improvements in type hinting support, I think shortening the support timespan a bit is reasonable, beneficial and proportional.
More important, NEP 29 is adopted by many other projects, providing a signal to the ecosystem that's okay to focus on the latest Python versions. The overlap between users using a Python version more than 2.5 years old and wanting the latest features and performance is probably pretty small. Older NumPy and other project's releases will won't disappear and be usable when the Python requirements are lifted.
I don't think this is true. These graphs show that Python 3.7 is the version with the most downloaded wheels for, and our 1.21.x downloads are still higher than our 1.22.x downloads - very likely because we dropped 3.7 support in 1.22.x: https://pypistats.org/packages/numpy https://pepy.tech/project/numpy?versions=1.22.2&versions=1.22.3&versions=1.22.4&versions=1.21.6&versions=1.20.3 I have seen problems popping up already in a few places with latest numpy not supported what is still the most commonly used Python version (don't have links, sorry - but they were real packaging-related issues). So I don't think it makes sense to shorten the time window. I also don't think there's a need to drop one version that's urgent - it's some effort and CI time, but the balance is decent right now. Cheers, Ralf
In my opinion a 30 month support window would provide a nice balance. Each Python version is supported for over 2 minor releases, with updates on the maintenance branches extending that with another few months. You can comfortably stay a full minor Python version behind year-round and still use the latest NumPy. For even older Python versions the old NumPy releases will stay available, and it allow NumPy to move on to new Python features a full year earlier then with a 42 month support window. Which not only improves feature and performance adaptation, but also lowers maintenance, testing, backporting and CI effort.
Ewout ter Hoeven (EwoutH) _______________________________________________ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-leave@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: ralf.gommers@gmail.com