You can use the following basic syntax to create a pivot table in pandas that displays the sum of values in certain columns:

pd.pivot_table(df, values='col1', index='col2', columns='col3', aggfunc='sum')

The following example shows how to use this syntax in practice.

**Example: Create Pandas Pivot Table With Sum of Values**

Suppose we have the following pandas DataFrame that contains information about various basketball players:

import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'], 'position': ['G', 'G', 'F', 'F', 'G', 'F', 'F', 'F'], 'points': [4, 4, 6, 8, 9, 5, 5, 12]}) #view DataFrame print(df) team position points 0 A G 4 1 A G 4 2 A F 6 3 A F 8 4 B G 9 5 B F 5 6 B F 5 7 B F 12

The following code shows how to create a pivot table in pandas that shows the sum of ‘points’ values for each ‘team’ and ‘position’ in the DataFrame:

#create pivot table df_pivot = pd.pivot_table(df, values='points', index='team', columns='position', aggfunc='sum') #view pivot table print(df_pivot) position F G team A 14 8 B 22 9

From the output we can see:

- Players on team A in position F scored a total of
**14**points. - Players on team A in position G scored a total of
**8**points. - Players on team B in position F scored a total of
**22**points. - Players on team B in position G scored a total of
**9**points.

Note that we can also use the **margins** argument to display the margin sums in the pivot table:

#create pivot table with margins df_pivot = pd.pivot_table(df, values='points', index='team', columns='position', aggfunc='sum', margins=True, margins_name='Sum') #view pivot table print(df_pivot) position F G Sum team A 14 8 22 B 22 9 31 Sum 36 17 53

The pivot table now displays the row sums and column sums.

**Note**: You can find the complete documentation for the pandas **pivot_table()** function here.

**Additional Resources**

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

Pandas: How to Reshape DataFrame from Long to Wide

Pandas: How to Reshape DataFrame from Wide to Long

Pandas: How to Group and Aggregate by Multiple Columns