Often you may be interested in finding the max value by group in a pandas DataFrame.

Fortunately this is easy to do using the **groupby()** and **max()** functions with the following syntax:

df.groupby('column_name').max()

This tutorial explains several examples of how to use this function in practice using the following pandas DataFrame:

import pandas as pd #create pandas DataFrame df = pd.DataFrame({'team': ['A', 'A', 'B', 'B', 'B', 'C', 'C'], 'points':[24, 23, 27, 11, 14, 8, 13], 'rebounds': [11, 8, 7, 6, 6, 5, 12]}) #display DataFrame print(df) team points rebounds 0 A 24 11 1 A 23 8 2 B 27 7 3 B 11 6 4 B 14 6 5 C 8 5 6 C 13 12

**Example 1: Max Value of Multiple Columns Grouped by One Variable**

The following code shows how to find the max value of multiple columns, grouped by one variable in a DataFrame:

#find max values of points and rebounds, grouped by team df.groupby('team').max().reset_index() team points rebounds 0 A 24 11 1 B 27 7 2 C 13 12

From the output we can see that:

- Team A has a max
*points*value of 24 and a max*rebounds*value of 11. - Team B has a max
*points*value of 27 and a max*rebounds*value of 7. - Team C has a max
*points*value of 13 and a max*rebounds*value of 12.

Note that we used the **reset_index()** function to ensure that the index matches the index in the original DataFrame.

**Example 2: ****Max Value of a Single Column Grouped by One Variable**

The following code shows how to find the max value of just one column, grouped on a single variable:

#find max value of points, grouped by team df.groupby('team')['points'].max().reset_index() team points 0 A 24 1 B 27 2 C 13

**Example 3: ****Sort by Max Values**

We can also use the **sort_values()** function to sort the max values.

We can specify **ascending=False** to sort from largest to smallest:

#find max value by team, sort descending df.groupby('team')['points'].max().reset_index().sort_values(['points'], ascending=False) team points 1 B 27 0 A 24 2 C 13

Or we can specify **ascending=True** to sort from smallest to largest:

#find max value by team, sort ascending df.groupby('team')['points'].max().reset_index().sort_values(['points'], ascending=True) team points 2 C 13 0 A 24 1 B 27

**Additional Resources**

How to Calculate the Sum of Columns in Pandas

How to Calculate the Mean of Columns in Pandas

How to Find the Max Value of Columns in Pandas