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Preserving boolean dtype in Series.any/all function with level keyword #33449 #33543

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Preserving boolean dtype in Series.any/all function with level keyword #33449 #33543

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14 changes: 9 additions & 5 deletions pandas/core/generic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9974,11 +9974,15 @@ def _agg_by_level(self, name, axis=0, level=0, skipna=True, **kwargs):
raise ValueError("Must specify 'axis' when aggregating by level.")
grouped = self.groupby(level=level, axis=axis, sort=False)
if hasattr(grouped, name) and skipna:
return getattr(grouped, name)(**kwargs)
axis = self._get_axis_number(axis)
method = getattr(type(self), name)
applyf = lambda x: method(x, axis=axis, skipna=skipna, **kwargs)
return grouped.aggregate(applyf)
result = getattr(grouped, name)(**kwargs)
else:
axis = self._get_axis_number(axis)
method = getattr(type(self), name)
applyf = lambda x: method(x, axis=axis, skipna=skipna, **kwargs)
result = grouped.aggregate(applyf)
if isinstance(self, ABCSeries) and self.dtype.name == "boolean":
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can you instead trace exactly where this is not getting converted propertly; likely its in EA method itself. We can to handle much lower down rather than a specific case here

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In pandas/core/groupby/groupby.py file, there is a _get_cythonized_result() function which has takes a pre_processing lambda where the return type of result is decided.
We can have an if/else statement there which checks the dtype of input and decides the dtype of the result.

return result.astype("boolean")
return result

@classmethod
def _add_numeric_operations(cls):
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9 changes: 7 additions & 2 deletions pandas/tests/reductions/test_reductions.py
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Expand Up @@ -912,8 +912,13 @@ def test_all_any_boolean(self):
index=[0, 0, 1, 1, 2, 2],
dtype="boolean",
)
tm.assert_series_equal(s.all(level=0), Series([False, True, False]))
tm.assert_series_equal(s.any(level=0), Series([False, True, True]))
result = s.all(level=0)
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can you pull L910 out to a new test. Then parameterize on bool and boolean.

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okay

expected = Series([False, True, False], dtype="boolean")
tm.assert_series_equal(result, expected)

result = s.any(level=0)
expected = Series([False, True, True], dtype="boolean")
tm.assert_series_equal(result, expected)

def test_timedelta64_analytics(self):

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