You can use the following methods to replace **inf** and **-inf** values with the max value in a pandas DataFrame:

**Method 1: Replace inf with Max Value in One Column**

#find max value of column max_value = np.nanmax(df['my_column'][df['my_column'] != np.inf]) #replace inf and -inf in column with max value of column df['my_column'].replace([np.inf, -np.inf], max_value, inplace=True)

**Method 2: Replace inf with Max Value in All Columns**

#find max value of entire data frame max_value = np.nanmax(df[df != np.inf]) #replace inf and -inf in all columns with max value df.replace([np.inf, -np.inf], max_value, inplace=True)

The following examples show how to use this syntax in practice with the following pandas DataFrame:

**import pandas as pd
import numpy as np
#create DataFrame
df = pd.DataFrame({'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
print(df)
points assists rebounds
0 18.0 5.0 inf
1 inf 7.0 8.0
2 19.0 7.0 10.0
3 inf 9.0 6.0
4 14.0 12.0 6.0
5 11.0 9.0 -inf
6 20.0 9.0 9.0
7 28.0 inf 12.0
**

**Example 1: Replace inf with Max Value in One Column**

The following code shows how to replace the **inf** and **-inf** values in the rebounds column with the max value of the rebounds column:

**#find max value of rebounds
max_value = np.nanmax(df['rebounds'][df['rebounds'] != np.inf])
#replace inf and -inf in rebounds with max value of rebounds
df['rebounds'].replace([np.inf, -np.inf], max_value, inplace=True)
#view updated DataFrame
print(df)
points assists rebounds
0 18.0 5.0 12.0
1 inf 7.0 8.0
2 19.0 7.0 10.0
3 inf 9.0 6.0
4 14.0 12.0 6.0
5 11.0 9.0 12.0
6 20.0 9.0 9.0
7 28.0 inf 12.0**

Notice that each **inf** and **-inf** value in the rebounds column has been replaced with the max value in that column of **12**.

**Example 2: Replace inf with Max Value in All Columns**

The following code shows how to replace the **inf** and **-inf** values in every column with the max value of the entire data frame:

**#find max value of entire data frame
max_value = np.nanmax(df[df != np.inf])
#replace all inf and -inf with max value
df.replace([np.inf, -np.inf], max_value, inplace=True)
#view updated DataFrame
print(df)
points assists rebounds
0 18.0 5.0 28.0
1 28.0 7.0 8.0
2 19.0 7.0 10.0
3 28.0 9.0 6.0
4 14.0 12.0 6.0
5 11.0 9.0 28.0
6 20.0 9.0 9.0
7 28.0 28.0 12.0
**

Notice that each **inf** and **-inf** value in every column has been replaced with the max value in the entire data frame of **28**.

**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