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  • timedelta64[D] is always coerced to timedelta64[ns] · 2 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
417381010 https://github.com/pydata/xarray/issues/1143#issuecomment-417381010 https://api.github.com/repos/pydata/xarray/issues/1143 MDEyOklzc3VlQ29tbWVudDQxNzM4MTAxMA== shoyer 1217238 2018-08-30T16:26:20Z 2018-08-30T16:26:20Z MEMBER

df_trans['DELTA'].dt.days should work, in both pandas in xarray.

 
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   timedelta64[D] is always coerced to timedelta64[ns] 192325490
263642220 https://github.com/pydata/xarray/issues/1143#issuecomment-263642220 https://api.github.com/repos/pydata/xarray/issues/1143 MDEyOklzc3VlQ29tbWVudDI2MzY0MjIyMA== shoyer 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.

 
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   timedelta64[D] is always coerced to timedelta64[ns] 192325490

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