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  • Allow for All-NaN in argmax, argmin · 4 ✖

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604838173 https://github.com/pydata/xarray/issues/3884#issuecomment-604838173 https://api.github.com/repos/pydata/xarray/issues/3884 MDEyOklzc3VlQ29tbWVudDYwNDgzODE3Mw== shoyer 1217238 2020-03-27T06:31:08Z 2020-03-27T06:31:08Z MEMBER

I wouldn’t worry too much about reusing bottleneck here, unless we really these functions will be the bottleneck in user code :)

 
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   Allow for All-NaN in argmax, argmin 587062505
604225711 https://github.com/pydata/xarray/issues/3884#issuecomment-604225711 https://api.github.com/repos/pydata/xarray/issues/3884 MDEyOklzc3VlQ29tbWVudDYwNDIyNTcxMQ== shoyer 1217238 2020-03-26T04:42:37Z 2020-03-26T04:42:37Z MEMBER

NumPy implements nanargmax, including raising the error, in Python. It would be very doable to copy a modified version into xarray.

 
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   Allow for All-NaN in argmax, argmin 587062505
604186105 https://github.com/pydata/xarray/issues/3884#issuecomment-604186105 https://api.github.com/repos/pydata/xarray/issues/3884 MDEyOklzc3VlQ29tbWVudDYwNDE4NjEwNQ== shoyer 1217238 2020-03-26T02:07:27Z 2020-03-26T02:07:27Z MEMBER

The main concern here is type stability. Normally the return value of argmax is an integer dtype array, but NaN isn't a valid integer :(

My suggestion would be to add an optional fill_value argument, similar to the API under discussion for idxmax in https://github.com/pydata/xarray/pull/3871. If fill_value is specified (e.g., with fill_value=np.nan or fill_value=-1) then missing values are returned with the fill value instead of raising an error.

 
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   Allow for All-NaN in argmax, argmin 587062505
603341320 https://github.com/pydata/xarray/issues/3884#issuecomment-603341320 https://api.github.com/repos/pydata/xarray/issues/3884 MDEyOklzc3VlQ29tbWVudDYwMzM0MTMyMA== max-sixty 5635139 2020-03-24T16:18:54Z 2020-03-24T16:18:54Z MEMBER

I think this would be a reasonable change

 
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   Allow for All-NaN in argmax, argmin 587062505

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