pull_requests: 171396650
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| id | node_id | number | state | locked | title | user | body | created_at | updated_at | closed_at | merged_at | merge_commit_sha | assignee | milestone | draft | head | base | author_association | auto_merge | repo | url | merged_by |
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| 171396650 | MDExOlB1bGxSZXF1ZXN0MTcxMzk2NjUw | 1942 | closed | 0 | Fix precision drop when indexing a datetime64 arrays. | 6815844 | - [x] Closes #1932 - [x] Tests added - [x] Tests passed - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API This precision drop was caused when converting `pd.Timestamp` to `np.array` ```python In [7]: ts = pd.Timestamp(np.datetime64('2018-02-12 06:59:59.999986560')) In [11]: np.asarray(ts, 'datetime64[ns]') Out[11]: array('2018-02-12T06:59:59.999986000', dtype='datetime64[ns]') ``` We need to call `to_datetime64` explicitly. | 2018-02-26T14:53:57Z | 2018-06-08T01:21:07Z | 2018-02-27T01:13:45Z | 2018-02-27T01:13:45Z | d8ccc7a999dce1a9ac205452e327bab5aa5f99f0 | 0 | f58aaa046d64315ae231fa77d7aa9e6713628742 | f530e668fa50665245988be2a00748b9b3ccc0a8 | MEMBER | 13221727 | https://github.com/pydata/xarray/pull/1942 |
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