issue_comments: 218358050

@rabernat - Sorry this took so long. Comments as I play around with the new feature... 1. I was getting some strange memory errors when trying this multidimensional groupbyon a large 4d ocean dataset (nlat: 720, nlon: 1280, time: 424, z_t: 45). my IPython Kernel just kept dying. Command was ds.TEMP.groupby('TLONG'). My naive guess is that this was a memory issue where a large number of bins were created - I think your first checkbox above alluded to this possibility. 2. Operating on a 2d field (dropped time and z_t dims), I get the following error:

``` pytb ----> 1 da.groupby('TLAT', bins=[50, 60, 70, 80, 90])

/Users/jhamman/Dropbox/src/xarray/xarray/core/common.py in groupby(self, group, squeeze, bins) 352 if isinstance(group, basestring): 353 group = self[group] --> 354 return self.groupby_cls(self, group, squeeze=squeeze, bins=bins) 355 356 def rolling(self, min_periods=None, center=False, **windows):

/Users/jhamman/Dropbox/src/xarray/xarray/core/groupby.py in init(self, obj, group, squeeze, grouper, bins) 141 if not index.is_monotonic: 142 # TODO: sort instead of raising an error --> 143 raise ValueError('index must be monotonic for resampling') 144 s = pd.Series(np.arange(index.size), index) 145 if grouper is not None:

ValueError: index must be monotonic for resampling ```

It seems this is only an issue when I specify bins. I see that there is a TODO statement there so maybe that will fix this.

Based on the datasets I have handy right now, I think number 2 in my list is a show show stopper so I think we want to make sure that feature makes it into this PR.