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BUG: error when unstacking in DataFrameGroupby.apply #52446

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Merged
merged 3 commits into from
Apr 6, 2023
Merged

BUG: error when unstacking in DataFrameGroupby.apply #52446

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889e939
BUG: error when unstacking in DataFrameGroupby.apply
topper-123 Apr 5, 2023
020fcc3
fix pre-commit
topper-123 Apr 5, 2023
0edff6e
adjust whatsnew for comments
topper-123 Apr 6, 2023
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v2.1.0.rst
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Expand Up @@ -295,6 +295,7 @@ Groupby/resample/rolling
or :class:`PeriodIndex`, and the ``groupby`` method was given a function as its first argument,
the function operated on the whole index rather than each element of the index. (:issue:`51979`)
- Bug in :meth:`GroupBy.var` failing to raise ``TypeError`` when called with datetime64 or :class:`PeriodDtype` values (:issue:`52128`)
- Bug in :meth:`DataFrameGroupBy.apply` when the supplied callable unstacked the grouped :class:`DataFrame` and returned a :class:`Series`, an attempt was made to set a list as the name of the result ``Series``, causing an error to be raised (:issue:`52444`)
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@rhshadrach rhshadrach Apr 5, 2023 •
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I think this bug occurs a bit more generally - anytime the input DataFrame is subset as a DataFrame after groupby ([['a']] and not ['a']) and the UDF returns Series that aren't all indexed the same.

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I’ve reformulated.

-

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8 changes: 7 additions & 1 deletion pandas/core/groupby/groupby.py
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Expand Up @@ -1192,7 +1192,13 @@ def _concat_objects(
else:
result = concat(values, axis=self.axis)

name = self.obj.name if self.obj.ndim == 1 else self._selection
if self.obj.ndim == 1:
name = self.obj.name
elif is_hashable(self._selection):
name = self._selection
else:
name = None

if isinstance(result, Series) and name is not None:
result.name = name

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20 changes: 20 additions & 0 deletions pandas/tests/groupby/test_apply.py
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Expand Up @@ -1203,6 +1203,26 @@ def test_groupby_apply_shape_cache_safety():
tm.assert_frame_equal(result, expected)


def test_groupby_apply_to_series_name():
# GH52444
df = DataFrame.from_dict(
{
"a": ["a", "b", "a", "b"],
"b1": ["aa", "ac", "ac", "ad"],
"b2": ["aa", "aa", "aa", "ac"],
}
)
grp = df.groupby("a")[["b1", "b2"]]
result = grp.apply(lambda x: x.unstack().value_counts())

expected_idx = MultiIndex.from_arrays(
arrays=[["a", "a", "b", "b", "b"], ["aa", "ac", "ac", "ad", "aa"]],
names=["a", None],
)
expected = Series([3, 1, 2, 1, 1], index=expected_idx, name="count")
tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("dropna", [True, False])
def test_apply_na(dropna):
# GH#28984
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