issue_comments

@rabernat - I'm a bit late to the party here but it looks like you have gotten it straightened out. I would have suggested plotting the projected data using Cartopy.

@clarkfitzg - this is the exact functionality we want with 2d plot coordinates and we definitely do not want to change it. It is a little annoying that pcolormesh wraps the x coordinate in the way it does but such is life.

@rabernat - the monthly-means example was developed in an ipython notebook and then exported to *.rst. The dataset in that example doesn't have lat/lon coordinates although it should. I'll see if I can add them this afternoon .

@rabernat - I just had a look through the code and it looks pretty good. I have a few broader questions though: 1. You have a few outstanding todo items from the first comment in your PR:

• [ ] Allow specification of which dims to stack. For example, stack in space but keep time dimension intact. (Currently it just stacks all the dimensions of the group variable.)
• [ ] A nice example for the docs.

Where do we stand on these? You have some simple examples in the docs now but maybe you were thinking of more complete examples? 2. In https://github.com/pydata/xarray/pull/818#issuecomment-218358050, I ran into the index is monotonic issue, it sounds like that was resolved. Do we cover that case in a test?

@rabernat - See link to 2d slice with coordinates below:

sample_for_xarray_multigroupby.nc.zip

As for the TODO, I see now that it was there before and I agree that we should be able to side step the sorted requirement.

@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.

This looks really promising. I've gone through the code for the first time and had just a few comments. I'll pull your branch down and give it a test drive on some real data.

@rabernat - I don't have much to add right now but I've very excited about this addition. Once you've filled in few more of the features, ping me and I'll give it a full review and will test it out in some applications we have in house.