html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
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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