You can use the following syntax to create a pivot table in pandas and provide multiple values to the **aggfunc** argument:

df.pivot_table(index='col1', values='col2', aggfunc=('sum', 'mean'))

This particular example creates a pivot table that displays the sum and the mean of values in **col2**, grouped by **col1**.

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

**Example: Create Pandas Pivot Table with Multiple aggfunc**

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', 'C', 'C', 'C', 'C'], 'points': [4, 4, 2, 8, 9, 5, 5, 7, 8, 8, 4, 3], 'assists': [2, 2, 5, 5, 4, 7, 5, 3, 9, 8, 4, 4]}) #view DataFrame print(df) team points assists 0 A 4 2 1 A 4 2 2 A 2 5 3 A 8 5 4 B 9 4 5 B 5 7 6 B 5 5 7 B 7 3 8 C 8 9 9 C 8 8 10 C 4 4 11 C 3 4

We can use the following code to create a pivot table that summarizes both the sum and the mean number of **points** scored by each **team**:

#create pivot table to summarize sum and mean of points by team df.pivot_table(index='team', values='points', aggfunc=('sum', 'mean')) mean sum team A 4.50 18 B 6.50 26 C 5.75 23

The resulting pivot table summarizes the mean and the sum of the points scored by each team.

For example, we can see:

- Players on team
**A**had a mean points value of**4.50**and a sum points value of**18**. - Players on team
**B**had a mean points value of**6.50**and a sum points value of**26**. - Players on team
**C**had a mean points value of**5.75**and a sum points value of**23**.

Note that we aggregated using the sum and the mean in this example, but we could also aggregate by other metrics such as:

- count
- min
- max
- median
- std (standard deviation)

The following example shows how to aggregate the values in the **points** column by these metrics for each team:

#create pivot table to summarize several metrics for points by team df.pivot_table(index='team', values='points', aggfunc=('count', 'min', 'max', 'median', 'std')) count max median min std team A 4 8 4.0 2 2.516611 B 4 9 6.0 5 1.914854 C 4 8 6.0 3 2.629956

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

**Additional Resources**

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