Create contingency table Pandas with counts and percentages

Is there a better way to create a contingency table in pandas with pd.crosstab() or pd.pivot_table() to generate counts and percentages.

Current solution

cat=['A','B','B','A','B','B','A','A','B','B']
target = [True,False,False,False,True,True,False,True,True,True]

import pandas as pd
df=pd.DataFrame({'cat' :cat,'target':target})

using crosstab

totals=pd.crosstab(df['cat'],df['target'],margins=True).reset_index()
percentages = pd.crosstab(df['cat'],
   df['target']).apply(lambda row: row/row.sum(),axis=1).reset_index()

and a merge

summaryTable=pd.merge(totals,percentages,on="cat")
summaryTable.columns=['cat','#False',
    '#True','All','percentTrue','percentFalse']

output

+---+-----+--------+-------+-----+-------------+--------------+
|   | cat | #False | #True | All | percentTrue | percentFalse |
+---+-----+--------+-------+-----+-------------+--------------+
| 0 | A   |      2 |     2 |   4 | 0.500000    | 0.500000     |
| 1 | B   |      2 |     4 |   6 | 0.333333    | 0.666667     |
+---+-----+--------+-------+-----+-------------+--------------+