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/364#issuecomment-231021167,https://api.github.com/repos/pydata/xarray/issues/364,231021167,MDEyOklzc3VlQ29tbWVudDIzMTAyMTE2Nw==,7504461,2016-07-07T08:54:46Z,2016-07-07T08:59:15Z,NONE,"Thanks, @shoyer !

Here is an example of how I circumvented the problem:

`data  = np.random.rand(24*5)`
`times = pd.date_range('2000-01-01', periods=24*5, freq='H')`
`foo    = xray.DataArray(data, coords=[times], dims=['time'])`
`foo    = foo.to_dataset(dim=foo.dims,name='foo')`

`T     = time.mktime( dt.datetime(1970,1,1,12+1,25,12).timetuple() ) # 12.42 hours`
`Tint   = [ int( time.mktime( t.timetuple() ) / T ) for t in  foo.time.values.astype('datetime64[s]').tolist()]`
`foo2  = xray.DataArray( Tint, coords=foo.time.coords, dims=foo.time.dims)`
`foo.merge(foo2.to_dataset(name='Tint'), inplace=True)`

`foo_grp = foo.groupby('Tint')`

`foo_grp.group.plot.line()`

In my case, the `dataset` is quite large then it costed a lot of computational time to merge the new variable `Tint`.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,60303760
https://github.com/pydata/xarray/issues/364#issuecomment-228723336,https://api.github.com/repos/pydata/xarray/issues/364,228723336,MDEyOklzc3VlQ29tbWVudDIyODcyMzMzNg==,7504461,2016-06-27T11:45:09Z,2016-06-27T11:45:09Z,NONE,"This is a very useful functionality. I am wondering if I can specify the time window, for example, like `ds.groupby(time=pd.TimeGrouper('12.42H'))`. Is there a way to do that in `xarray`?
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,60303760