issue_comments: 263642220
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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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 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 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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