Dear Sage developers,

the NumPy enhancement proposal 29: "Recommend Python and Numpy version 
support as a community policy standard" (available at 
https://numpy.org/neps/nep-0029-deprecation_policy.html) specifies when 
it's okay to drop support for old Python version. 

Namely, a release should support "all minor versions of Python released 42 
months prior to the project, and at minimum the two latest minor versions. 
". In particular, this means:
- Currently, Sage should support > 3.8.
- On Apr 05, 2024 we should drop support for Python 3.9 (initially released 
on Oct 05, 2020)

Based on previous discussions on this topic 
(https://groups.google.com/g/sage-devel/c/j1cwbTU8aOU/m/2sTiwdKPBQAJ, 
https://github.com/sagemath/sage/issues/30384, 
https://github.com/sagemath/sage/pull/35403), I'm calling for a vote on 
adapting the Python version support of NEP 29 in Sage. Voting ends at the 
7th June,  AoE. Please use this thread only for sending votes, to make it 
easier to count them afterward; and use the thread 
https://groups.google.com/g/sage-devel/c/j1cwbTU8aOU/m/2sTiwdKPBQAJ for 
discussion.

*Summary *of the points brought forward in the discussions linked above
1. The current practice in Sage is to evaluate whether to increase the 
minimum version of Python supported at the beginning of each release cycle. 
With regard to such a practice, the NEP 29 documents remarks "As there is 
no objective threshold to when the minimum version should be dropped, it is 
easy for these version support discussions to devolve into bike shedding 
<https://en.wikipedia.org/wiki/Wikipedia:Avoid_Parkinson%27s_bicycle-shed_effect>
 
and acrimony." Sadly, an example of this can be found in the current 
discussion of dropping Python 3.8 support in 
https://github.com/sagemath/sage/pull/35404 with emotions running so high 
that sage-abuse had to step in. Adopting a version policy would prevent 
such discussions. On the other hand, by following a given policy, we would 
loose some flexibility.
2. The main idea of NEP 29 is to have a community-wide standard. It is 
followed by many scientific packages such as Scipy, Matplotlib, IPython, 
Jupyter, Pandas, scikit, astropy, cuda, cirq, jax, pytorch among others. The 
adoption of NEP 29 will harmonize Sage's deprecation policy with these 
other major libraries. 
3. The NEP 29 drop schedule is much faster than the EOL schedule of Python 
itself. Python 3.8 is supported until 2024-10, but NEP 29 already drops it 
2023-04. However, adhering to the EOL schedule would prevent us to updating 
these packages that follow NEP 29.
4. The NEP 29 schedule is about one release cycle faster than the previous 
drops (e.g. Python 3.7 support was dropped in Sage 9.7 while according to 
NEP 29 it would have been Sage 9.6).
5. The faster drop schedule will free developer resources (less systems to 
test) and potentially increase developer productivity as it allows us to 
use newer language features.
6. The faster drop schedule might be inconvenient for users who rely on 
older Python versions. To some extend this is remedied by our python 
install package, and relatively straightforward upgrade paths on most 
system. One should also note that users relying on other scientific python 
packages are likely forced to upgrade anyway, since these other packages 
likely follow NEP 29.
7. The faster drop schedule would force users to upgrade to newer Python 
versions and thereby profit from fewer bugs and security issues. It is 
however questionable if Sage should play this educator role.
8. One of the main goals of NEP 29 is to improve downstream project 
planning by having a community-wide standard. This is currently not very 
relevant for us as Sage is currently upstream of nothing except for some 
user packages. With the modularization effort, this may change in the 
future.
9. There are not many other documented policies. As said above, most 
scientific python projects follow NEP 29. Most projects in web development 
(e.g flask) seem to drop a version once it reaches EOL. Machine learning 
projects follow a similar EOL policy (e.g. tensorflow) or roughly follow 
NEP 29 (scikit-learn). Some end-user applications have even stricter 
version constraints then NEP 29 (e.g. home-assistant only supports the two 
latest minor releases).

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