Inconsistent behaviour of GroupBy for BooleanArray series #58031
Description
Lets suppose aggregate function returns int or float. Then if it returns only 0 and 1 then result is converted to BooleanArray. Otherwise, it returns int or float arrays (as expected).
This is because this code is preserving type if series values is not a subclass of np.ndarray type. And BooleanArray is not.
pandas/pandas/core/groupby/ops.py
Line 917 in b552dc9
So then the code tries to preserve type if it can.
Code to reproduce
df = pd.DataFrame({0: [1, 2, 2], 1: [True, False, None]})
df[1] = df[1].astype("boolean")
print(df.groupby(by=0).aggregate(lambda s: s.fillna(False).mean()).dtypes.values[0])
prints boolean.
If we change values in array
df = pd.DataFrame({0: [1, 2, 2], 1: [True, True, None]})
df[1] = df[1].astype("boolean")
print(df.groupby(by=0).aggregate(lambda s: s.fillna(False).mean()).dtypes.values[0])
then it prints float64.
If dtype is "bool" (not "boolean"), then groupby always returns expected float result.
df = pd.DataFrame({0: [1, 2, 2], 1: [True, False, None]})
df[1] = df[1].astype("bool")
print(df.groupby(by=0).aggregate(lambda s: s.fillna(False).mean()).dtypes.values[0])
prints float64