issues: 764440458

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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
764440458 MDExOlB1bGxSZXF1ZXN0NTM4NTAzNDk3 4684 Ensure maximum accuracy when encoding and decoding np.datetime64[ns] values 6628425 closed 0     3 2020-12-12T21:43:57Z 2021-02-07T23:30:41Z 2021-01-03T23:39:04Z MEMBER   0 pydata/xarray/pulls/4684
  • [x] Closes #4045
  • [x] Tests added
  • [x] Passes isort . && black . && mypy . && flake8
  • [x] User visible changes (including notable bug fixes) are documented in whats-new.rst

This PR cleans up the logic used to encode and decode times with pandas so that by default we use int64 values in both directions for all precisions down to nanosecond. If a user specifies an encoding (or a file is read in) such that float values would be required, things still work as they did before. I do this mainly by following the approach I described here: https://github.com/pydata/xarray/issues/4045#issuecomment-626257580.

In the process of doing this I made a few changes to coding.times._decode_datetime_with_pandas: - I removed the checks on the minimum and maximum dates to decode, as the issue those checks were imposed for (#975) was fixed in pandas way back in 2016 (https://github.com/pandas-dev/pandas/issues/14068). - I used an alternate approach for fixing #2002, which allows us to continue to use the optimization made in #1414 without having to cast the input array to a float dtype first.

Note this will change the default units that are chosen for encoding times in some instances -- previously we would never default to anything more precise than seconds -- but I think this change is for the better.

cc: @aldanor

@hmaarrfk this overlaps a little with your work in #4400, so I'm giving you credit here too (I hope you don't mind!).

 
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