How can I map True/False to 1/0 in a Pandas DataFrame?

python

pandas

dataframe

data-type-conversion

byNikita Barsukov·Feb 10, 2025

To instantly transform boolean to integers in a Pandas DataFrame, use df = df.astype(int) for an entire DataFrame or df['col'] = df['col'].astype(int) for a specific column. This maps True to 1, False to 0.

# For one lucky column only:
df['col'] = df['col'].astype(int)

# Roll the dice for the entire DataFrame:
df = df.astype(int)

Additional methods and techniques

The minimalistic approach: Multiply by 1

This concise operation multiplies the DataFrame or column by 1:

# It's a match! Multiply by 1:
df['col'] = df['col'] * 1

# Say "I DO" to the entire DataFrame:
df = df * 1

It's kung-fu style Zen minimalism at its best, swiftly converting booleans without explicit data type conversion.

The communicator: In-place replacement

When sharing code, be the coder who communicates. Use df.replace() for explicit declaration of your intentions:

df.replace({False: 0, True: 1}, inplace=True)

Think about it as changing clothes but keeping your clothes on!

The booleans' secret keeper: Direct calculations

Python secretly already treats True as 1 and False as 0. So for many operations, you don't even need to unmask them at all!

result = sum(df['col'])  # Sure feels like a secret handshake!

This is super handy for analysis, statistics, and other undercover operations.

The detective: Post-conversion checks

After any transformation, confirm the identity of your DataFrame or column:

# Check the aliases:
print(df.dtypes)

Remember, failing to verify might lead you into detective noir style troubles!

Dive deeper

The Magician: np.where

For some magic tricks, use numpy.where:

import numpy as np
df['col'] = np.where(df['col'], 1, 0) # np.where == Hogwarts for your DataFrame

This trick allows for complex situations tailored to fit more than just true/false cases.

The Scholar: df.apply

df['col'] = df['col'].apply(lambda x: 1 if x else 0)  # Philosophical: To be 1 or not to be 0

This technique is kind of a Swiss knife: applicable for more complex situations and comes with free introspection!

The Cartographer: df.map

A mapping dictionary for clarity:

df['col'] = df['col'].map({False: 0, True: 1})  # Huh, that was clearly a map!

Now, isn't that a readable map to your code treasure!

Be Alert: Common traps

When mapping booleans, watch out for:

Unwanted prisoners: Accidentally transforming non-targeted columns.
Ghosts in your DataFrames: Ignoring null values or non-boolean datatypes.
Tripping on shoelaces: Overwriting columns by mistake during in-place operations.