import pandas as pd
import numpy as np

#Create a dataframe with a categorical column with two categories and a (numpy) boolean column that is randomly True or False
df = pd.DataFrame.from_dict({'category':['A']*10+['B']*10, 
                             'bool_numpy': np.random.rand(20)>0.5})

#Now make another column that is a copy of the numpy boolean column, but converted to pyarrow
df['bool_arrow'] = df['bool_numpy'].astype('bool[pyarrow]')

print(df.head())
#   category  bool_numpy bool_arrow
# 0        A        True       True
# 1        A        True       True
# 2        A        True       True
# 3        A        True       True
# 4        A       False      False

#Now do a gruopby and aggregate to compute the fraction of True values in each column:
true_fracs = df.groupby('category').agg(lambda x: x.sum()/x.count())

print(true_fracs)

#          bool_numpy bool_arrow
# category                       
# A                0.7       True
# B                0.6       True

#I expect both columns above to have identical floating-point values, not boolean.

print(df.query("category=='A'")[['bool_numpy','bool_arrow']].sum()/df[['bool_numpy','bool_arrow']].count())
# bool_numpy    0.7
# bool_arrow    0.7
# dtype: float64