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id node_id number title user state locked assignee milestone comments created_at updated_at ▲ closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
1888576440 I_kwDOAMm_X85wkWO4 8162 Update group by multi index dcherian 2448579 open 0     0 2023-09-09T04:50:29Z 2023-09-09T04:50:39Z   MEMBER      

ideally GroupBy._infer_concat_args() would return a xr.Coordinates object that contains both the coordinate(s) and their (multi-)index to assign to the result (combined) object.

The goal is to avoid calling create_default_index_implicit(coord) below where coord is a pd.MultiIndex or a single IndexVariable wrapping a multi-index. If coord is a Coordinates object, we could do combined = combined.assign_coords(coord) instead.

https://github.com/pydata/xarray/blob/e2b6f3468ef829b8a83637965d34a164bf3bca78/xarray/core/groupby.py#L1573-L1587

There are actually more general issues:

  • The group parameter of Dataset.groupby being a single variable or variable name, it won't be possible to do groupby on a full pandas multi-index once we drop its dimension coordinate (#8143). How can we still support it? Maybe passing a dimension name to group and check that there's only one index for that dimension?
  • How can we support custom, multi-coordinate indexes with groupby? I don't have any practical example in mind, but in theory just passing a single coordinate name as group will invalidate the index. Should we drop the index in the result? Or, like suggested above pass a dimension name as group and check the index?

Originally posted by @benbovy in https://github.com/pydata/xarray/issues/8140#issuecomment-1709775666

 
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     xarray 13221727 issue

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