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/pull/2458#issuecomment-460019498,https://api.github.com/repos/pydata/xarray/issues/2458,460019498,MDEyOklzc3VlQ29tbWVudDQ2MDAxOTQ5OA==,1217238,2019-02-03T03:21:52Z,2019-02-03T03:21:52Z,MEMBER,Implemented in https://github.com/pydata/xarray/pull/2593,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,365961291
https://github.com/pydata/xarray/pull/2458#issuecomment-427056750,https://api.github.com/repos/pydata/xarray/issues/2458,427056750,MDEyOklzc3VlQ29tbWVudDQyNzA1Njc1MA==,1217238,2018-10-04T15:12:07Z,2018-10-04T15:12:07Z,MEMBER,"I never saw clear use-cases for TimeGrouper but I could be convinced.
On Thu, Oct 4, 2018 at 2:51 PM David Huard <notifications@github.com> wrote:

> Do you think there would be a benefit to implementing a TimeGrouper class
> based on panda's ?
>
> —
> You are receiving this because you authored the thread.
> Reply to this email directly, view it on GitHub
> <https://github.com/pydata/xarray/pull/2458#issuecomment-427006309>, or mute
> the thread
> <https://github.com/notifications/unsubscribe-auth/ABKS1rj2dc2Fk1el8e5kjU5VQGs5759jks5uhgRKgaJpZM4XEUir>
> .
>
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,365961291
https://github.com/pydata/xarray/pull/2458#issuecomment-427054269,https://api.github.com/repos/pydata/xarray/issues/2458,427054269,MDEyOklzc3VlQ29tbWVudDQyNzA1NDI2OQ==,6628425,2018-10-04T15:05:20Z,2018-10-04T15:05:20Z,MEMBER,"> Do you think there would be a benefit to implementing a TimeGrouper class based on panda's ?

My instinct would be to first pursue the simple approach that @shoyer has started here.  If it turns out that passing a `pandas.Series` rather than a `pandas.Grouper` instance in line 236 of `groupby.py` prevents us from replicating some important behavior of resample, then it might be something to think about.  

As of yet, while there are a few details that need to be added to Stephan's implementation (e.g., as he notes in the to-do comment, proper handling of the `closed`, `label`, and `base` arguments; there is some other complexity regarding how to handle gaps in the time series, etc.), I do not (yet) see any reason why these couldn't be handled with some modifications to the current approach.  The logic in `TimeGrouper` is definitely a good reference for how to handle the different arguments to resample, but if we can, I think it would be nice to avoid the complexity of defining a new `Grouper` class.  ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,365961291
https://github.com/pydata/xarray/pull/2458#issuecomment-427006309,https://api.github.com/repos/pydata/xarray/issues/2458,427006309,MDEyOklzc3VlQ29tbWVudDQyNzAwNjMwOQ==,81219,2018-10-04T12:51:21Z,2018-10-04T12:51:21Z,CONTRIBUTOR,Do you think there would be a benefit to implementing a TimeGrouper class based on panda's ? ,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,365961291
https://github.com/pydata/xarray/pull/2458#issuecomment-426681357,https://api.github.com/repos/pydata/xarray/issues/2458,426681357,MDEyOklzc3VlQ29tbWVudDQyNjY4MTM1Nw==,1217238,2018-10-03T15:30:45Z,2018-10-03T15:30:45Z,MEMBER,"Inspired by @spencerkclark's suggestion, I tried another version based on `cftime_range` and reindex with `method='pad'`. This one seems to be working in more cases:
```python
In [7]: times = xarray.cftime_range('2000', periods=30, freq='MS')

In [8]: da = xarray.DataArray(range(30), [('time', times)])

In [9]: times
Out[9]:
CFTimeIndex([2000-01-01 00:00:00, 2000-02-01 00:00:00, 2000-03-01 00:00:00,
             2000-04-01 00:00:00, 2000-05-01 00:00:00, 2000-06-01 00:00:00,
             2000-07-01 00:00:00, 2000-08-01 00:00:00, 2000-09-01 00:00:00,
             2000-10-01 00:00:00, 2000-11-01 00:00:00, 2000-12-01 00:00:00,
             2001-01-01 00:00:00, 2001-02-01 00:00:00, 2001-03-01 00:00:00,
             2001-04-01 00:00:00, 2001-05-01 00:00:00, 2001-06-01 00:00:00,
             2001-07-01 00:00:00, 2001-08-01 00:00:00, 2001-09-01 00:00:00,
             2001-10-01 00:00:00, 2001-11-01 00:00:00, 2001-12-01 00:00:00,
             2002-01-01 00:00:00, 2002-02-01 00:00:00, 2002-03-01 00:00:00,
             2002-04-01 00:00:00, 2002-05-01 00:00:00, 2002-06-01 00:00:00],
            dtype='object')

In [10]: da.resample(time='12MS').mean()
Out[10]:
<xarray.DataArray (time: 3)>
array([ 5.5, 17.5, 26.5])
Coordinates:
  * time     (time) object 2000-01-01 00:00:00 ... 2002-01-01 00:00:00

In [11]: da.resample(time='6MS').mean()
Out[11]:
<xarray.DataArray (time: 5)>
array([ 2.5,  8.5, 14.5, 20.5, 26.5])
Coordinates:
  * time     (time) object 2000-01-01 00:00:00 ... 2002-01-01 00:00:00

In [12]: da.resample(time='3MS').mean()
Out[12]:
<xarray.DataArray (time: 10)>
array([ 1.,  4.,  7., 10., 13., 16., 19., 22., 25., 28.])
Coordinates:
  * time     (time) object 2000-01-01 00:00:00 ... 2002-04-01 00:00:00
```","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,365961291