NEP 29 — Recommend Python and Numpy version support as a community policy standard


Thomas A Caswell <>, Andreas Mueller, Brian Granger, Madicken Munk, Ralf Gommers, Matt Haberland <>, Matthias Bussonnier <>, Stefan van der Walt <>









This NEP recommends that all projects across the Scientific Python ecosystem adopt a common “time window-based” policy for support of Python and NumPy versions. Standardizing a recommendation for project support of minimum Python and NumPy versions will improve downstream project planning.

This is an unusual NEP in that it offers recommendations for community-wide policy and not for changes to NumPy itself. Since a common place for SPEEPs (Scientific Python Ecosystem Enhancement Proposals) does not exist and given NumPy’s central role in the ecosystem, a NEP provides a visible place to document the proposed policy.

This NEP is being put forward by maintainers of Matplotlib, scikit-learn, IPython, Jupyter, yt, SciPy, NumPy, and scikit-image.

Detailed description

For the purposes of this NEP we assume semantic versioning and define:

major version

A release that changes the first number (e.g. X.0.0)

minor version

A release that changes the second number (e.g 1.Y.0)

patch version

A release that changes the third number (e.g. 1.1.Z)

When a project releases a new major or minor version, we recommend that they support at least all minor versions of Python introduced and released in the prior 42 months from the anticipated release date with a minimum of 2 minor versions of Python, and all minor versions of NumPy released in the prior 24 months from the anticipated release date with a minimum of 3 minor versions of NumPy.

Consider the following timeline:

     Jan 16      Jan 17      Jan 18      Jan 19      Jan 20
     |           |           |           |           |
 |              |                  |               |
 py 3.5.0       py 3.6.0           py 3.7.0        py 3.8.0
|-----------------------------------------> Feb19
          |-----------------------------------------> Dec19
                    |-----------------------------------------> Nov20

It shows the 42 month support windows for Python. A project with a major or minor version release in February 2019 should support Python 3.5 and newer, a project with a major or minor version released in December 2019 should support Python 3.6 and newer, and a project with a major or minor version release in November 2020 should support Python 3.7 and newer.

The current Python release cadence is 18 months so a 42 month window ensures that there will always be at least two minor versions of Python in the window. The window is extended 6 months beyond the anticipated two-release interval for Python to provide resilience against small fluctuations / delays in its release schedule.

Because Python minor version support is based only on historical release dates, a 42 month time window, and a planned project release date, one can predict with high confidence when a project will be able to drop any given minor version of Python. This, in turn, could save months of unnecessary maintenance burden.

If a project releases immediately after a minor version of Python drops out of the support window, there will inevitably be some mismatch in supported versions—but this situation should only last until other projects in the ecosystem make releases.

Otherwise, once a project does a minor or major release, it is guaranteed that there will be a stable release of all other projects that, at the source level, support the same set of Python versions supported by the new release.

If there is a Python 4 or a NumPy 2 this policy will have to be reviewed in light of the community’s and projects’ best interests.

Support Table




Jan 07, 2020



Jun 23, 2020



Jul 23, 2020



Jan 13, 2021



Jul 26, 2021



Dec 22, 2021



Dec 26, 2021



Jun 21, 2022



Apr 14, 2023



Drop Schedule

On next release, drop support for Python 3.5 (initially released on Sep 13, 2015)
On Jan 07, 2020 drop support for Numpy 1.14 (initially released on Jan 06, 2018)
On Jun 23, 2020 drop support for Python 3.6 (initially released on Dec 23, 2016)
On Jul 23, 2020 drop support for Numpy 1.15 (initially released on Jul 23, 2018)
On Jan 13, 2021 drop support for Numpy 1.16 (initially released on Jan 13, 2019)
On Jul 26, 2021 drop support for Numpy 1.17 (initially released on Jul 26, 2019)
On Dec 22, 2021 drop support for Numpy 1.18 (initially released on Dec 22, 2019)
On Dec 26, 2021 drop support for Python 3.7 (initially released on Jun 27, 2018)
On Jun 21, 2022 drop support for Numpy 1.19 (initially released on Jun 20, 2020)
On Apr 14, 2023 drop support for Python 3.8 (initially released on Oct 14, 2019)


We suggest that all projects adopt the following language into their development guidelines:

This project supports:

  • All minor versions of Python released 42 months prior to the project, and at minimum the two latest minor versions.

  • All minor versions of numpy released in the 24 months prior to the project, and at minimum the last three minor versions.

In, the python_requires variable should be set to the minimum supported version of Python. All supported minor versions of Python should be in the test matrix and have binary artifacts built for the release.

Minimum Python and NumPy version support should be adjusted upward on every major and minor release, but never on a patch release.

Backward compatibility

No backward compatibility issues.


Ad-Hoc version support

A project could, on every release, evaluate whether to increase the minimum version of Python supported. As a major downside, an ad-hoc approach makes it hard for downstream users to predict what the future minimum versions will be. 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 and acrimony.

All CPython supported versions

The CPython supported versions of Python are listed in the Python Developers Guide and the Python PEPs. Supporting these is a very clear and conservative approach. However, it means that there exists a four year lag between when a new features is introduced into the language and when a project is able to use it. Additionally, for projects with compiled extensions this requires building many binary artifacts for each release.

For the case of NumPy, many projects carry workarounds to bugs that are fixed in subsequent versions of NumPy. Being proactive about increasing the minimum version of NumPy allows downstream packages to carry fewer version-specific patches.

Default version on Linux distribution

The policy could be to support the version of Python that ships by default in the latest Ubuntu LTS or CentOS/RHEL release. However, we would still have to standardize across the community which distribution to follow.

By following the versions supported by major Linux distributions, we are giving up technical control of our projects to external organizations that may have different motivations and concerns than we do.

N minor versions of Python

Given the current release cadence of the Python, the proposed time (42 months) is roughly equivalent to “the last two” Python minor versions. However, if Python changes their release cadence substantially, any rule based solely on the number of minor releases may need to be changed to remain sensible.

A more fundamental problem with a policy based on number of Python releases is that it is hard to predict when support for a given minor version of Python will be dropped as that requires correctly predicting the release schedule of Python for the next 3-4 years. A time-based rule, in contrast, only depends on past events and the length of the support window.

Time window from the X.Y.1 Python release

This is equivalent to a few month longer support window from the X.Y.0 release. This is because X.Y.1 bug-fix release is typically a few months after the X.Y.0 release, thus a N month window from X.Y.1 is roughly equivalent to a N+3 month from X.Y.0.

The X.Y.0 release is naturally a special release. If we were to anchor the window on X.Y.1 we would then have the discussion of why not X.Y.M?


References and Footnotes

Code to generate support and drop schedule tables

from datetime import datetime, timedelta

data = """Jan 15, 2017: Numpy 1.12
Sep 13, 2015: Python 3.5
Dec 23, 2016: Python 3.6
Jun 27, 2018: Python 3.7
Jun 07, 2017: Numpy 1.13
Jan 06, 2018: Numpy 1.14
Jul 23, 2018: Numpy 1.15
Jan 13, 2019: Numpy 1.16
Jul 26, 2019: Numpy 1.17
Oct 14, 2019: Python 3.8
Dec 22, 2019: Numpy 1.18
Jun 20, 2020: Numpy 1.19

releases = []

plus42 = timedelta(days=int(365*3.5 + 1))
plus24 = timedelta(days=int(365*2 + 1))

for line in data.splitlines():
    date, project_version = line.split(':')
    project, version = project_version.strip().split(' ')
    release = datetime.strptime(date, '%b %d, %Y')
    if project.lower() == 'numpy':
        drop = release + plus24
        drop = release + plus42
    releases.append((drop, project, version, release))

releases = sorted(releases, key=lambda x: x[0])

py_major,py_minor = sorted([int(x) for x in r[2].split('.')] for r in releases if r[1] == 'Python')[-1]
minpy = f"{py_major}.{py_minor+1}+"

num_major,num_minor = sorted([int(x) for x in r[2].split('.')] for r in releases if r[1] == 'Numpy')[-1]
minnum = f"{num_major}.{num_minor+1}+"

toprint_drop_dates = ['']
toprint_support_table = []
for d, p, v, r in releases[::-1]:
    df = d.strftime('%b %d, %Y')
        f'On {df} drop support for {p} {v} '
        f'(initially released on {r.strftime("%b %d, %Y")})')
    toprint_support_table.append(f'{df} {minpy:<6} {minnum:<5}')
    if p.lower() == 'numpy':
        minnum = v+'+'
        minpy = v+'+'
print("On next release, drop support for Python 3.5 (initially released on Sep 13, 2015)")
for e in toprint_drop_dates[-4::-1]:

print('============ ====== =====')
print('Date         Python NumPy')
print('------------ ------ -----')
for e in toprint_support_table[-4::-1]:
print('============ ====== =====')