issue_comments: 341598412

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https://github.com/pydata/xarray/issues/364#issuecomment-341598412 https://api.github.com/repos/pydata/xarray/issues/364 341598412 MDEyOklzc3VlQ29tbWVudDM0MTU5ODQxMg== 14136435 2017-11-03T00:40:14Z 2017-11-03T00:40:39Z CONTRIBUTOR

Hi, being able to pass a pd.TimeGrouper to .groupby() would be really handy. Here is my use-case and work around at the moment (.resample() doesn't serve my needs because I need to iterate over the groups):

```python import pandas as pd import xarray as xr

dates = pd.DatetimeIndex(['2017-01-01 15:00', '2017-01-02 14:00', '2017-01-02 23:00']) da = xr.DataArray([1, 2, 3], dims=['time'], coords={'time': dates}) time_grouper = pd.TimeGrouper(freq='24h', base=15)

digging around the source code for xr.DataArray.resample i found this

grouped = xr.core.groupby.DataArrayGroupBy(da, 'time', grouper=time_grouper)

for _, sub_da in grouped: print(sub_da) ```

which prints: <xarray.DataArray (time: 2)> array([1, 2]) Coordinates: * time (time) datetime64[ns] 2017-01-01T15:00:00 2017-01-02T14:00:00 <xarray.DataArray (time: 1)> array([3]) Coordinates: * time (time) datetime64[ns] 2017-01-02T23:00:00

Would it be possible to add a grouper kwarg to .groupby(), e.g. python da.groupby('time', grouper=time_grouper)

 
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