html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/1143#issuecomment-417381010,https://api.github.com/repos/pydata/xarray/issues/1143,417381010,MDEyOklzc3VlQ29tbWVudDQxNzM4MTAxMA==,1217238,2018-08-30T16:26:20Z,2018-08-30T16:26:20Z,MEMBER,"`df_trans['DELTA'].dt.days` should work, in both pandas in xarray.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,192325490
https://github.com/pydata/xarray/issues/1143#issuecomment-263642220,https://api.github.com/repos/pydata/xarray/issues/1143,263642220,MDEyOklzc3VlQ29tbWVudDI2MzY0MjIyMA==,1217238,2016-11-29T17:40:49Z,2016-11-29T17:40:49Z,MEMBER,"Interesting. Pandas always uses nanosecond precision for `TimedeltaIndex` but not `Series`:
```
In [13]: s = pandas.Series([1,2,3,4]).astype('timedelta64[D]')

In [14]: s
Out[14]:
0   1 days
1   2 days
2   3 days
3   4 days
dtype: timedelta64[D]

In [16]: pandas.Index(s)
Out[16]: TimedeltaIndex(['1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq=None)
```

This might actually be a pandas bug -- as far as I recall, this goes against the documented behavior.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,192325490