[backport 2.3.x] BUG: fix fill value for gouped sum in case of unobserved categories for string dtype (empty string instead of 0) (#61909) #61963
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…or string dtype (empty string instead of 0) (pandas-dev#61909)
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| if using_string_dtype() and method == "sum": | ||
| if isinstance(output, Series) and isinstance(output.dtype, StringDtype): | ||
| d["fill_value"] = "" | ||
| return output.reindex(**d) # type: ignore[arg-type] | ||
| elif isinstance(output, DataFrame) and any( | ||
| isinstance(dtype, StringDtype) for dtype in output.dtypes | ||
| ): | ||
| orig_dtypes = output.dtypes | ||
| indices = np.nonzero(output.dtypes == "string")[0] | ||
| for idx in indices: | ||
| output.isetitem(idx, output.iloc[:, idx].astype(object)) | ||
| output = output.reindex(**d) | ||
| for idx in indices: | ||
| col = output.iloc[:, idx] | ||
| output.isetitem( | ||
| idx, col.mask(col == 0, "").astype(orig_dtypes.iloc[idx]) | ||
| ) | ||
| return output |
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The original fix from the PR does not actually work for the 2.3.x branch, because here we still have the _reindex_output helper method to deal with the observed=False case.
And then we have the problem that output.reindex(..) doesn't work if you want to fill with multiple fill values (0 for numerical columns, "" for string columns). The above is a bit an ugly solution, but I also don't directly know a cleaner way how to do this with the current pandas APIs .. (and given this is only for 2.3.x and not to keep forever, and only behind the option flag, it's maybe OK?)
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Yea - I don't see any nice solution here, I'm good with the above. Just have the CI failing.
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Backport of #61909