How to Use idxmax() Function in Pandas (With Examples)

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You can use the pandas.DataFrame.idxmax() function to return the index of the maximum value across a specified axis in a pandas DataFrame.

This function uses the following syntax:

DataFrame.idxmax(axis=0, skipna=True)

where:

• axis: The axis to use (0 = rows, 1 = columns). Default is 0.
• skipna: Whether or not to exclude NA or null values. Default is True.

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

import pandas as pd

#create DataFrame
df = pd.DataFrame({'points': [25, 12, 15, 8, 9, 23],
                   'assists': [5, 7, 7, 9, 12, 9],
                   'rebounds': [11, 8, 11, 6, 6, 5]},
                   index=['Andy','Bob', 'Chad', 'Dan', 'Eric', 'Frank'])

#view DataFrame
df

        points	assists	rebounds
Andy	25	5	11
Bob	12	7	8
Chad	15	7	11
Dan	8	9	6
Eric	9	12	6
Frank	23	9	5

Example 1: Find Index that has Max Value for Each Column

The following code shows how to find the index that has the maximum value for each column:

#find index that has max value for each column
df.idxmax(axis=0)

points      Andy
assists     Eric
rebounds    Andy
dtype: object

From the output we can see:

• The player with the highest value in the points column is Andy.
• The player with the highest value in the assists column is Eric.
• The player with the highest value in the rebounds column is Andy.

It’s important to note that the idxmax() function will return the first occurrence of the maximum value.

For example, notice that Andy and Chad both had 11 rebounds. Since Andy appears first in the DataFrame his name is returned.

Example 2: Find Column that has Max Value for Each Row

The following code shows how to find the column that has the maximum value for each row:

#find column that has max value for each row
df.idxmax(axis=1)

Andy      points
Bob       points
Chad      points
Dan      assists
Eric     assists
Frank     points
dtype: object

From the output we can see:

• The highest value in the row labelled “Andy” can be found in the points column.
• The highest value in the row labelled “Bob” can be found in the points column.
• The highest value in the row labelled “Chad” can be found in the points column.
• The highest value in the row labelled “Dan” can be found in the assists column.
• The highest value in the row labelled “Eric” can be found in the assists column.
• The highest value in the row labelled “Andy” can be found in the points column.

Refer to the pandas documentation for a complete explanation of the idxmax() function.

Additional Resources

How to Find the Max Value of Columns in Pandas
How to Calculate the Mean of Columns in Pandas
How to Calculate the Sum of Columns in Pandas