issue_comments: 426250976
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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 |
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https://github.com/pydata/xarray/issues/2450#issuecomment-426250976 | https://api.github.com/repos/pydata/xarray/issues/2450 | 426250976 | MDEyOklzc3VlQ29tbWVudDQyNjI1MDk3Ng== | 6628425 | 2018-10-02T12:16:03Z | 2018-10-02T12:16:03Z | MEMBER | Thanks @am-thyst, this happens to be expected behavior. For a dimension indexed by a DatetimeIndex (e.g. your As you inferred, if you add detail to the string you provide, you can refine the selection. That said, you need to be careful of the format you use (I think the reason you get a In [2]: times = pd.date_range('1979', periods=8, freq='12H') In [3]: da = xr.DataArray(np.arange(8), coords=[times], dims=['time']) In [4]: da Out[4]: <xarray.DataArray (time: 8)> array([0, 1, 2, 3, 4, 5, 6, 7]) Coordinates: * time (time) datetime64[ns] 1979-01-01 ... 1979-01-04T12:00:00 In [5]: da.loc['1979-01-01T00']
Out[5]:
<xarray.DataArray ()>
array(0)
Coordinates:
time datetime64[ns] 1979-01-01
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