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  • Loss of coordinate information from groupby.apply() on a stacked object · 5 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
703191101 https://github.com/pydata/xarray/issues/1483#issuecomment-703191101 https://api.github.com/repos/pydata/xarray/issues/1483 MDEyOklzc3VlQ29tbWVudDcwMzE5MTEwMQ== stale[bot] 26384082 2020-10-04T02:32:44Z 2020-10-04T02:32:44Z NONE

In order to maintain a list of currently relevant issues, we mark issues as stale after a period of inactivity

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   Loss of coordinate information from groupby.apply() on a stacked object 244016361
316773753 https://github.com/pydata/xarray/issues/1483#issuecomment-316773753 https://api.github.com/repos/pydata/xarray/issues/1483 MDEyOklzc3VlQ29tbWVudDMxNjc3Mzc1Mw== shoyer 1217238 2017-07-20T17:25:27Z 2017-07-20T17:25:27Z MEMBER

This wasn't intentional. If we can fix it in a straightforward fashion, we definitely should.

 
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   Loss of coordinate information from groupby.apply() on a stacked object 244016361
316381473 https://github.com/pydata/xarray/issues/1483#issuecomment-316381473 https://api.github.com/repos/pydata/xarray/issues/1483 MDEyOklzc3VlQ29tbWVudDMxNjM4MTQ3Mw== byersiiasa 17701232 2017-07-19T13:12:03Z 2017-07-19T13:12:03Z NONE

@darothen yes you are right - this is definitely not a good way to apply mean - I was just using mean as a (poor) example trying not to over-complicate or distract from the issue. But, as you suggest, this is what I do when needing to apply customised functions like from scipy... which, can end up being slow.

 
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   Loss of coordinate information from groupby.apply() on a stacked object 244016361
316377854 https://github.com/pydata/xarray/issues/1483#issuecomment-316377854 https://api.github.com/repos/pydata/xarray/issues/1483 MDEyOklzc3VlQ29tbWVudDMxNjM3Nzg1NA== darothen 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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   Loss of coordinate information from groupby.apply() on a stacked object 244016361
316363418 https://github.com/pydata/xarray/issues/1483#issuecomment-316363418 https://api.github.com/repos/pydata/xarray/issues/1483 MDEyOklzc3VlQ29tbWVudDMxNjM2MzQxOA== byersiiasa 17701232 2017-07-19T12:00:42Z 2017-07-19T12:00:42Z NONE

** Maybe not an issue for others or I am missing something... Or perhaps this is intended behaviour? Thanks for clarification!

 
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   Loss of coordinate information from groupby.apply() on a stacked object 244016361

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