issue_comments: 208092684

@shoyer, @jhamman I think this is ready for a review

There are two distinct features added here: 1. groupby works with multidimensional coordinate variables. (See example at the top of the PR.) 2. groupby accepts a new keyword group_bins, which is passed to pandas.cut to digitize the groups (have not documented this yet because I could use some feedback on the api). For now, the coordinates are labeled with the category labels determined by cut. Using the example array above

``` python

da.groupby('lat', bins=[0,15,20]).apply(lambda x : x.sum()) <xarray.DataArray (lat: 2)> array([1, 5]) Coordinates: * lat (lat) object '(0, 15]' '(15, 20]' ```

I'm not sure this is the ideal behavior, since the categories are hard to slice. For my purposes, I would rather assign an integer or float index to each bin using e.g. the central value of the bin.

note: Both of these features have problems when used with shortcut=True.