You can use the following syntax to replace inf and -inf values with zero in a pandas DataFrame:

df.replace([np.inf, -np.inf], 0, inplace=True)

The following example shows how to use this syntax in practice.

**Example: Replace inf with Zero in Pandas**

Suppose we have the following pandas DataFrame that contains information about various basketball players:

import pandas as pd import numpy as np #create DataFrame df = pd.DataFrame({'team': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'], 'points': [18, np.inf, 19, np.inf, 14, 11, 20, 28], 'assists': [5, 7, 7, 9, 12, 9, 9, np.inf], 'rebounds': [np.inf, 8, 10, 6, 6, -np.inf, 9, 12]}) #view DataFrame df team points assists rebounds 0 A 18.0 5.0 inf 1 B inf 7.0 8.0 2 C 19.0 7.0 10.0 3 D inf 9.0 6.0 4 E 14.0 12.0 6.0 5 F 11.0 9.0 -inf 6 G 20.0 9.0 9.0 7 H 28.0 inf 12.0

Notice that there are several inf and -inf values throughout the DataFrame.

We can use the following syntax to replace these inf and -inf values with zero:

#replace inf and -inf with zero df.replace([np.inf, -np.inf], 0, inplace=True) #view updated DataFrame df team points assists rebounds 0 A 18.0 5.0 0.0 1 B 0.0 7.0 8.0 2 C 19.0 7.0 10.0 3 D 0.0 9.0 6.0 4 E 14.0 12.0 6.0 5 F 11.0 9.0 0.0 6 G 20.0 9.0 9.0 7 H 28.0 0.0 12.0

Notice that each of the inf and -inf values have been replaced with zero.

**Note**: You can find the complete documentation for the **replace** function in pandas here.

**Additional Resources**

The following tutorials explain how to perform other common tasks in pandas:

How to Impute Missing Values in Pandas

How to Count Missing Values in Pandas

How to Fill NaN Values with Mean in Pandas