How to replace inf with Max Value in Pandas dataframe?

---

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.