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/267#issuecomment-60707841,https://api.github.com/repos/pydata/xarray/issues/267,60707841,MDEyOklzc3VlQ29tbWVudDYwNzA3ODQx,1217238,2014-10-28T04:15:04Z,2014-10-28T04:15:04Z,MEMBER,"Closing this, but please let me know if you notice any other apparent inconsistencies.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,46756880
https://github.com/pydata/xarray/issues/267#issuecomment-60426317,https://api.github.com/repos/pydata/xarray/issues/267,60426317,MDEyOklzc3VlQ29tbWVudDYwNDI2MzE3,1217238,2014-10-24T18:06:23Z,2014-10-24T18:06:23Z,MEMBER,"We pass off all label interpretation to pandas, so this discussion is really about what pandas does. The partial string selection is indeed a pandas feature: http://pandas.pydata.org/pandas-docs/stable/timeseries.html#datetimeindex-partial-string-indexing
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,46756880
https://github.com/pydata/xarray/issues/267#issuecomment-60425242,https://api.github.com/repos/pydata/xarray/issues/267,60425242,MDEyOklzc3VlQ29tbWVudDYwNDI1MjQy,291576,2014-10-24T17:58:37Z,2014-10-24T17:58:37Z,CONTRIBUTOR,"So, is the string approach I used above to grab a single day's data a bug or a feature? It is a nice short-hand, but I don't want to rely on it if it isn't intended to be a feature. Similarly, if I supply a Year-Month string, I get data for that month.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,46756880
https://github.com/pydata/xarray/issues/267#issuecomment-60417045,https://api.github.com/repos/pydata/xarray/issues/267,60417045,MDEyOklzc3VlQ29tbWVudDYwNDE3MDQ1,1217238,2014-10-24T17:01:46Z,2014-10-24T17:01:46Z,MEMBER,"Try supplying the exact date and time.e.g, `datetime(2013, 1, 1, 11, 15)` (I think that's right), or doing a slice, e.g., `c.sel(time=slice(datetime(2013, 1, 1), datetime(2013, 1, 2)))`.
This should definitely work but pandas (reasonably) assumes that datetime objects refer to particular instants in time, unlike strings, which are less precise.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,46756880
https://github.com/pydata/xarray/issues/267#issuecomment-60413505,https://api.github.com/repos/pydata/xarray/issues/267,60413505,MDEyOklzc3VlQ29tbWVudDYwNDEzNTA1,291576,2014-10-24T16:37:26Z,2014-10-24T16:37:26Z,CONTRIBUTOR,"Gah, I am sorry, please disregard my last comment. I can't add/subtract...
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,46756880
https://github.com/pydata/xarray/issues/267#issuecomment-60413356,https://api.github.com/repos/pydata/xarray/issues/267,60413356,MDEyOklzc3VlQ29tbWVudDYwNDEzMzU2,291576,2014-10-24T16:36:18Z,2014-10-24T16:36:18Z,CONTRIBUTOR,"A bit of a further wrinkle is that date selection seems to be limited to local time only because of this limitation. Consider the following:
```
>>> c['time'][:25]
array(['2013-01-01T06:15:00.000000000-0500',
'2013-01-01T07:00:00.000000000-0500',
'2013-01-01T08:00:00.000000000-0500',
'2013-01-01T09:00:00.000000000-0500',
'2013-01-01T10:00:00.000000000-0500',
'2013-01-01T11:00:00.000000000-0500',
'2013-01-01T12:00:00.000000000-0500',
'2013-01-01T13:00:00.000000000-0500',
'2013-01-01T14:00:00.000000000-0500',
'2013-01-01T15:00:00.000000000-0500',
'2013-01-01T16:00:00.000000000-0500',
'2013-01-01T17:00:00.000000000-0500',
'2013-01-01T18:00:00.000000000-0500',
'2013-01-01T19:00:00.000000000-0500',
'2013-01-01T20:00:00.000000000-0500',
'2013-01-01T21:00:00.000000000-0500',
'2013-01-01T22:00:00.000000000-0500',
'2013-01-01T23:00:00.000000000-0500',
'2013-01-02T00:00:00.000000000-0500',
'2013-01-02T01:00:00.000000000-0500',
'2013-01-02T02:00:00.000000000-0500',
'2013-01-02T03:00:00.000000000-0500',
'2013-01-02T04:00:00.000000000-0500',
'2013-01-02T05:00:00.000000000-0500',
'2013-01-02T06:00:00.000000000-0500'], dtype='datetime64[ns]')
Coordinates:
* time (time) datetime64[ns] 2013-01-01T11:15:00 ...
latitude float32 64.833
elevation float32 137.5
longitude float32 -147.6
>>> c.sel(time='2013-01-01')['time']
array(['2013-01-01T06:15:00.000000000-0500',
'2013-01-01T07:00:00.000000000-0500',
'2013-01-01T08:00:00.000000000-0500',
'2013-01-01T09:00:00.000000000-0500',
'2013-01-01T10:00:00.000000000-0500',
'2013-01-01T11:00:00.000000000-0500',
'2013-01-01T12:00:00.000000000-0500',
'2013-01-01T13:00:00.000000000-0500',
'2013-01-01T14:00:00.000000000-0500',
'2013-01-01T15:00:00.000000000-0500',
'2013-01-01T16:00:00.000000000-0500',
'2013-01-01T17:00:00.000000000-0500',
'2013-01-01T18:00:00.000000000-0500'], dtype='datetime64[ns]')
Coordinates:
* time (time) datetime64[ns] 2013-01-01T11:15:00 ...
latitude float32 64.833
elevation float32 137.5
longitude float32 -147.6
```
I don't know how I would (easily) slice this data array such as to grab only data for a UTC day.
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