issues: 669307837

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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
669307837 MDExOlB1bGxSZXF1ZXN0NDU5Njk1NDA5 4292 Fix indexing with datetime64[ns] with pandas=1.1 1217238 closed 0     11 2020-07-31T00:48:50Z 2020-09-16T03:11:48Z 2020-09-16T01:33:30Z MEMBER   0 pydata/xarray/pulls/4292

Fixes #4283

The underlying issue is that calling .item() on a NumPy array with dtype=datetime64[ns] returns an integer, rather than an np.datetime64 scalar. This is somewhat baffling but works this way because .item() returns native Python types, but datetime.datetime doesn't support nanosecond precision.

pandas.Index.get_loc used to support these integers, but now is more strict. Hence we get errors.

We can fix this by using array[()] to convert 0d arrays into NumPy scalars instead of calling array.item().

I've added a crude regression test. There may well be a better way to test this but I haven't figured it out yet.

  • [x] Tests added
  • [x] Passes isort . && black . && mypy . && flake8
 
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