Pandas DataFrame.where()-Python

DataFrame.where() function replace values in a DataFrame based on a condition. It allows you to keep the original value where a condition is True and replace it with something else e.g., NaN or a custom value where the condition is False.

For Example:

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, -2, 3], 'B': [-1, 5, -6]})
res = df.where(df > 0)
print(res)

     A    B
0  1.0  NaN
1  NaN  5.0
2  3.0  NaN

Explanation: df.where(df > 0) keeps values where the condition is True (greater than 0) and replaces others with NaN.

Syntax

DataFrame.where(cond, other=nan, inplace=False, axis=None, level=None, errors='raise', try_cast=False)

Parameters:

Returns: A DataFrame with replaced values (or None if inplace=True)

Examples

Example 1: In this, we are replacing negative numbers with 0 using the other parameter.

import pandas as pd
df = pd.DataFrame({'A': [1, -2, 3], 'B': [-1, 5, -6]})
res = df.where(df > 0, other=0)
print(res)

   A  B
0  1  0
1  0  5
2  3  0

Explanation: where(df > 0, other=0) keeps values > 0 and replaces negatives with 0, leaving positives unchanged.

Example 2: In this, we are modifying the DataFrame directly to replace values ≤ 0 with NaN.

import pandas as pd
df = pd.DataFrame({'A': [2, -3, 4], 'B': [-5, 6, -7]})
df.where(df > 0, inplace=True)
print(df)

     A    B
0  2.0  NaN
1  NaN  6.0
2  4.0  NaN

Explanation: inplace=True applies the change directly to df without needing to assign it to a new variable.

Example 3: In this, we are applying a condition using a Series instead of the full DataFrame.

import pandas as pd
df = pd.DataFrame({'Score': [45, 85, 60, 30]})
condition = df['Score'] >= 50
res = df.where(condition, other='Fail')
print(res)

  Score
0  Fail
1    85
2    60
3  Fail

Explanation: Only scores ≥ 50 are kept and others are replaced with 'Fail'.

Related article: DataFrame