issue_comments: 316377854

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https://github.com/pydata/xarray/issues/1483#issuecomment-316377854 https://api.github.com/repos/pydata/xarray/issues/1483 316377854 MDEyOklzc3VlQ29tbWVudDMxNjM3Nzg1NA== 4992424 2017-07-19T12:59:04Z 2017-07-19T12:59:04Z NONE

Instead of computing the mean over your non-stacked dimension by

python dsg = dst.groupby('allpoints').mean()

why not just instead call

python dsg = dst.mean('time', keep_attrs=True)

so that you just collapse the time dimension and preserve the attributes on your data? Then you can unstack() and everything should still be there. The idiom of stacking/applying/unstacking is really useful to fit your data to the interface of a numpy or scipy function that will do all the heavy lifting with a vectorized routine for you - isn't using groupby in this way really slow? 

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