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  • dcherian · 1 ✖

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  • Support multi-dimensional grouped operations and group_over · 1 ✖

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  • MEMBER · 1 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1054569287 https://github.com/pydata/xarray/issues/324#issuecomment-1054569287 https://api.github.com/repos/pydata/xarray/issues/324 IC_kwDOAMm_X84-23NH dcherian 2448579 2022-02-28T19:03:17Z 2022-02-28T19:03:17Z MEMBER

I have this almost ready in flox (needs more tests). So we should be able to do this soon.

In the mean time note that we can view grouping over multiple variables as a "factorization" (group identification) problem for aggregations. That means you can 1. use pd.factorize, pd.cut, np.searchsorted or np.bincount to convert each by variable to an integer code, 2. then use np.ravel_multi_index to combine the codes to a single variable idx 3. Group by idx and accumulate 4. use np.unravel_index (or just a simple np.reshape) to convert the single grouped dimension to a multiple dimensions. 5. Construct output coordinate arrays.

 
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   Support multi-dimensional grouped operations and group_over 58117200

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