You can use the following basic syntax to add a filtering condition to a pandas pivot table:

df[df.col1 == 'A'].pivot_table(index='col1', values=['col2', 'col3'], aggfunc='sum')

This particular example creates a pivot table that displays the sum of values in **col2** and **col3**, grouped by **col1**.

The filter before the **pivot_table()** function specifies that we only want to include rows where the value in **col1** of the original DataFrame has a value of ‘A’.

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

**Example: How to Add Filter to Pandas Pivot Table**

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 in pandas that shows the sum of the values in the **points** and **assists** columns grouped by **team** only for the rows where the original DataFrame has a value in the **team** column equal to ‘A’:

#create pivot table for rows where team is equal to 'A' df[df.team == 'A'].pivot_table(index='team', values=['points', 'assists'], aggfunc='sum') assists points team A 14 18

Notice that the pivot table only summarizes the values in the **points** and **assists** columns for the rows where the **team** is equal to ‘A’.

You can also use the operators **&** and **|** to apply a filter that uses “AND” or “OR” logic.

For example, we can use the following syntax to create a pivot table that filters for rows where the value in the **team** column of the original DataFrame is equal to ‘A’ or ‘B’:

#create pivot table for rows where team is equal to 'A' or 'B' df[(df.team == 'A') | (df.team == 'B')].pivot_table(index='team', values=['points', 'assists'], aggfunc='sum') assists points team A 14 18 B 19 26

Notice that the pivot table only summarizes the values in the **points** and **assists** columns for the rows where the **team** is equal to ‘A’ or ‘B’.

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