BUG: groupby.agg with UDF changing pyarrow dtypes #59601
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added 30 commits
April 1, 2024 19:04
…unt for missing pyarrow dtypes
…b.com:undermyumbrella1/pandas into fix/group_by_agg_pyarrow_bool_numpy_same_type
rhshadrach
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rhshadrach
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…group_by_agg_pyarrow_bool_numpy_same_type
rhshadrach
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Mar 23, 2025
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| result = gb.agg(lambda x: {"number": 1}) | ||
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| arr = pa.array([{"number": 1}, {"number": 1}, {"number": 1}]) | ||
| expected = DataFrame( | ||
| {"B": ArrowExtensionArray(arr)}, | ||
| index=Index(["c1", "c2", "c3"], name="A"), | ||
| ) |
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When the column starts as a PyArrow dtype and returns dictionaries, it seems questionable to me whether we should return the corresponding PyArrow dtype. The other option is a NumPy array of object dtype. But both seem like reasonable results and I imagine the PyArrow is likely to be more convenient for the user who is using PyArrow dtypes.
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Continuation of #58129
doc/source/whatsnew/vX.X.X.rstfile if fixing a bug or adding a new feature.Root cause:
agg_seriesalways forces output dtype to be the same as input dtype, but depending on the lambda, the output dtype can be differentFix:
maybe_convert_object, asmaybe_convert_objectdoes not check for NA, and forces dtype to float if NA is present (NA is not float in pyarrow),