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  • mschrimpf · 2 ✖

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  • Efficient workaround to group by multiple dimensions · 2 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
426329058 https://github.com/pydata/xarray/issues/2438#issuecomment-426329058 https://api.github.com/repos/pydata/xarray/issues/2438 MDEyOklzc3VlQ29tbWVudDQyNjMyOTA1OA== mschrimpf 5308236 2018-10-02T15:56:53Z 2018-10-02T15:56:53Z NONE

I built a manual solution in the stackoverflow thread. Maybe this helps someone.

 
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   Efficient workaround to group by multiple dimensions 363629186
424444423 https://github.com/pydata/xarray/issues/2438#issuecomment-424444423 https://api.github.com/repos/pydata/xarray/issues/2438 MDEyOklzc3VlQ29tbWVudDQyNDQ0NDQyMw== mschrimpf 5308236 2018-09-25T18:06:11Z 2018-09-26T13:58:02Z NONE

Thanks @shoyer, Your comment helped me realize that at least part of the "horribly slow" probably stems from a DataArray with MultiIndex. The above code sample takes 5-6 seconds for 1000 b values. When stacking the DataArray beforehand with d = d.stack(adim=['a'], bdim=['b']), it takes around 14 seconds. However, both of these are unfortunately very slow compared to indexing in e.g. numpy or pandas.

 
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   Efficient workaround to group by multiple dimensions 363629186

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