issues: 645154872

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id node_id number title user state locked assignee milestone comments created_at updated_at closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
645154872 MDU6SXNzdWU2NDUxNTQ4NzI= 4179 Consider revising our minimum dependency version policy 1217238 closed 0     7 2020-06-25T05:04:38Z 2021-02-22T05:02:25Z 2021-02-22T05:02:25Z MEMBER      

Our current policy is that xarray supports "the minor version (X.Y) initially published no more than N months ago" where N is:

  • Python: 42 months (NEP 29)
  • numpy: 24 months (NEP 29)
  • pandas: 12 months
  • scipy: 12 months
  • sparse, pint and other libraries that rely on NEP-18 for integration: very latest available versions only,
  • all other libraries: 6 months

I think this policy is too aggressive, particularly for pandas, SciPy and other libraries. Some of these projects can go 6+ months between minor releases. For example, version 2.3 of zarr is currently more than 6 months old. So if zarr released 2.4 today and xarray issued a new release tomorrow, and then our policy would dictate that we could ask users to upgrade to the new version.

In https://github.com/pydata/xarray/pull/4178, I misinterpreted our policy as supporting "the most recent minor version (X.Y) initially published more than N months ago". This version makes a bit more sense to me: users only need to upgrade dependencies at least every N months to use the latest xarray release.

I understand that NEP-29 chose its language intentionally, so that distributors know ahead of time when they can drop support for a Python or NumPy version. But this seems like a (very) poor fit for projects without regular releases. At the very least we should adjust the specific time windows.

I'll see if I can gain some understanding of the motivation for this particular language over on the NumPy tracker...

 
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