# Pandas: Create Pivot Table with Multiple aggfunc

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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 .