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  • max-sixty · 5 ✖

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  • {DataArray,Dataset}.rank() should support an optional list of dimensions · 5 ✖

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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
592738965 https://github.com/pydata/xarray/issues/3810#issuecomment-592738965 https://api.github.com/repos/pydata/xarray/issues/3810 MDEyOklzc3VlQ29tbWVudDU5MjczODk2NQ== max-sixty 5635139 2020-02-28T21:33:35Z 2020-02-28T21:33:35Z MEMBER

Yeah, unfortunately I'm fairly confident about this; have a go with moderately large arrays for sum and you'll quickly see the performance cliff 

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   {DataArray,Dataset}.rank() should support an optional list of dimensions 572875480
592721162 https://github.com/pydata/xarray/issues/3810#issuecomment-592721162 https://api.github.com/repos/pydata/xarray/issues/3810 MDEyOklzc3VlQ29tbWVudDU5MjcyMTE2Mg== max-sixty 5635139 2020-02-28T20:47:33Z 2020-02-28T20:47:33Z MEMBER

Great -- that's cool and a good implementation of apply_ufunc. As above, we wouldn't want to replace rank with that given the reshaping (we'd need a function that computes over multiple dimensions)

We could use something similar for groupbys though?

 
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   {DataArray,Dataset}.rank() should support an optional list of dimensions 572875480
592708353 https://github.com/pydata/xarray/issues/3810#issuecomment-592708353 https://api.github.com/repos/pydata/xarray/issues/3810 MDEyOklzc3VlQ29tbWVudDU5MjcwODM1Mw== max-sixty 5635139 2020-02-28T20:13:51Z 2020-02-28T20:13:51Z MEMBER

Could you try running that?

 
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   {DataArray,Dataset}.rank() should support an optional list of dimensions 572875480
592665711 https://github.com/pydata/xarray/issues/3810#issuecomment-592665711 https://api.github.com/repos/pydata/xarray/issues/3810 MDEyOklzc3VlQ29tbWVudDU5MjY2NTcxMQ== max-sixty 5635139 2020-02-28T18:34:44Z 2020-02-28T18:34:44Z MEMBER

Yes, we can always reshape as a way of running numerical operations over multiple dimensions. But reshaping can be an expensive operation, so doing it as part of a numerical operation can cause surprises. (if you're interested, try running a sum over multiple dimensions and comparing to a reshape + a sum over the single reshaped dimension).

Instead, users can do this themselves, giving them context and control.

Reshaping is OK to do in groupby though (I think), so adding rank to groupby would be one way of accomplishing this.

 
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   {DataArray,Dataset}.rank() should support an optional list of dimensions 572875480
592645335 https://github.com/pydata/xarray/issues/3810#issuecomment-592645335 https://api.github.com/repos/pydata/xarray/issues/3810 MDEyOklzc3VlQ29tbWVudDU5MjY0NTMzNQ== max-sixty 5635139 2020-02-28T17:43:05Z 2020-02-28T17:43:05Z MEMBER

This would be great. The underlying numerical library we use, bottleneck, doesn't support multiple dimensions. If there were another option, or someone wanted to write one in numbagg, that would be a welcome addition.

 
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   {DataArray,Dataset}.rank() should support an optional list of dimensions 572875480

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