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Mapping True and False to 1 and 0 respectively in Pandas DataFrame

schedule Aug 10, 2023
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To map booleans True and False to 1 and 0 respectively, perform casting using astype(int):

df = pd.DataFrame({"A":[True, False, True]})
df = df["A"].astype(int)
df
0 1
1 0
2 1
Name: A, dtype: int64

However, astype(int) will throw an error when there are NaN values. To preserve the NaN values, use the Series' replace(~) function like so:

import numpy as np
df = pd.DataFrame({"A":[True, np.nan, False]})
df["A"] = df["A"].replace({True: 1, False: 0})
df
A
0 1.0
1 NaN
2 0.0
robocat
Published by Isshin Inada
Edited by 0 others
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