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  • sparse and other duck array issues · 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
780798734 https://github.com/pydata/xarray/issues/3245#issuecomment-780798734 https://api.github.com/repos/pydata/xarray/issues/3245 MDEyOklzc3VlQ29tbWVudDc4MDc5ODczNA== tacaswell 199813 2021-02-17T19:33:05Z 2021-02-17T19:33:05Z NONE

Is there any motion on this? This come up in relation to akward array today (see https://github.com/scikit-hep/awkward-1.0/issues/749 ).

If xarray goes with the to_numpy spelling as well then all of {pandas, xarray, akwardarray} have the same spelling which is nice from the Matplotlib side of things.

I think the as_ vs to_ distinction the "to_" makes more sense as it gets you out of xarray land and drops you back to raw numpy land.

 
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   sparse and other duck array issues 484240082
539215442 https://github.com/pydata/xarray/issues/3245#issuecomment-539215442 https://api.github.com/repos/pydata/xarray/issues/3245 MDEyOklzc3VlQ29tbWVudDUzOTIxNTQ0Mg== khaeru 1634164 2019-10-07T21:37:53Z 2019-10-07T21:37:53Z NONE

As far as I can tell, the proposal here will require either python s = pd.Series(...) xr.DataArray.from_series(s).to_series() or: python xr.DataArray.from_series(s, sparse=True).to_dense().to_series()

For any code that can't guarantee sparse/non-sparse input, the first will fail sometimes, so it will always be necessary to write the latter everywhere, which IMO is unnecessarily verbose.

 
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   sparse and other duck array issues 484240082

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