issue_comments: 535115143
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| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/pydata/xarray/issues/3222#issuecomment-535115143 | https://api.github.com/repos/pydata/xarray/issues/3222 | 535115143 | MDEyOklzc3VlQ29tbWVudDUzNTExNTE0Mw== | 6213168 | 2019-09-25T16:57:36Z | 2019-09-25T17:06:29Z | MEMBER | How does this sound as a rolling policy? - Python: latest 2 major versions published in official anaconda releases (as opposed to just the conda repository), plus the latest major version available version in conda-forge. Latest available minimum versions. This today translates to =3.6 and =3.7 (with no minor versions, so actually 3.6.7 and 3.7.4); 3.8 to be added as soon as conda-forge rebuilds its own stack with it. - numpy, pandas, scipy: latest major version at least 1 year old; latest available minor version (which may be newer than 1 year old). Today this translates to numpy=1.15, pandas=0.23, scipy=1.1. - all other non-NEP18 packages: 6 months old major versions, or the latest available in the anaconda (not conda-forge) repository if it exists, whatever is older - NEP18 packages (sparse, pint, etc.): very latest available only, at least until the technology has reasonably matured across the whole ecosystem. This extends to dask when used to wrap NEP18 arrays. numpy >=1.17. - In all cases: older versions than those stated by the above policy can remain officially supported indefinitely, but only as long as doing so can be achieved trivially and doesn't slow development down. |
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