issues: 33833155

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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
33833155 MDU6SXNzdWUzMzgzMzE1NQ== 136 More consistent datetime conversion 1217238 closed 0   836999 0 2014-05-19T20:16:32Z 2014-12-19T20:54:51Z 2014-12-19T05:27:50Z MEMBER      

Todo: - [x] Decide on rules for datetime conversion - [x] Implement them - [ ] Document them

Currently: - All np.datetime64 arrays or objects are converted to ns precision - We leave other datetime or datetime-like objects intact in numpy arrays of dtype=object.

Arguably, we should convert everything to 'datetime64[ns]', if possible. This is the approach we now take for decoding NetCDF time variables (#126).

Reference discussion: #134. From @akleeman:

In #125 I went the route of forcing datetimes to be datetime64[ns]. This is probably part of a broader conversation, but doing so might save some future headaches. Of course ... it would also restrict us to nanosecond precision. Basically I feel like we should either force datetimes to be datetime64[ns] or make sure that operations on datetime objects preserve their type.

Probably worth getting this in and picking that conversation back up if needed. In which case could you add tests which make sure variables with datetime objects are still datetime objects after concatenation? If those start getting cast to datetime[ns] it'll start get confusing for users.

Also worth considering: how should datetime64[us] datetimes be handled? Currently they get cast to [ns] which, since datetimes do not, could get confusing.

 
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