You can use the following methods to calculate the mean value by group in pandas:

**Method 1: Calculate Mean of One Column Grouped by One Column**

df.groupby(['group_col'])['value_col'].mean()

**Method 2: Calculate Mean of Multiple Columns Grouped by One Column**

df.groupby(['group_col'])['value_col1', 'value_col2'].mean()

**Method 3: Calculate Mean of One Column Grouped by Multiple Columns**

df.groupby(['group_col1', 'group_col2'])['value_col'].mean()

The following examples show how to use each method in practice with the following pandas DataFrame:

import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'], 'position': ['G', 'F', 'F', 'G', 'F', 'F', 'G', 'G'], 'points': [30, 22, 19, 14, 14, 11, 20, 28], 'assists': [4, 3, 7, 7, 12, 15, 8, 4]}) #view DataFrame print(df) team position points assists 0 A G 30 4 1 A F 22 3 2 A F 19 7 3 A G 14 7 4 B F 14 12 5 B F 11 15 6 B G 20 8 7 B G 28 4

**Example 1: Calculate Mean of One Column Grouped by One Column**

The following code shows how to calculate the mean value of the **points** column, grouped by the **team** column:

#calculate mean of points grouped by team df.groupby('team')['points'].mean() team A 21.25 B 18.25 Name: points, dtype: float64

From the output we can see:

- The mean points value for team A is
**21.25**. - The mean points value for team B is
**18.25**.

**Example 2: Calculate Mean of Multiple Columns Grouped by One Column**

The following code shows how to calculate the mean value of the **points** column and the mean value of the **assists** column, grouped by the **team** column:

#calculate mean of points and mean of assists grouped by team df.groupby('team')[['points', 'assists']].mean() points assists team A 21.25 5.25 B 18.25 9.75

The output displays the mean **points** value and mean **assists** value for each team.

**Example 3: Calculate Mean of One Column Grouped by Multiple Columns**

The following code shows how to calculate the mean value of the **points** column, grouped by the **team** and **position** columns:

#calculate mean of points, grouped by team and position df.groupby(['team', 'position'])['points'].mean() team position A F 20.5 G 22.0 B F 12.5 G 24.0 Name: points, dtype: float64

From the output we can see:

- The mean points value for players on team A and position F is
**20.5**. - The mean points value for players on team A and position G is
**22**. - The mean points value for players on team B and position F is
**12.5**. - The mean points value for players on team B and position G is
**24**.

**Additional Resources**

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

How to Find the Max Value by Group in Pandas

How to Find Sum by Group in Pandas

How to Calculate Quantiles by Group in Pandas

Hi. I’ve searched for hours trying to find the solution/methodology you demonstrate in Method 1. It works like a charm! Thank you so much!!!