You can use the following basic syntax to sort a pandas pivot table based on the values in a column:

my_pivot_table.sort_values(by=['some_column'], ascending=False)

This particular example sorts the values in a pivot table called my_pivot_table based on the values in some_column in descending order.

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

Example: Sort Pandas Pivot Table by Values in Column

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 for each team:

#create pivot table
df_pivot = df.pivot_table(index=['team'], values=['points', 'assists'], aggfunc='sum')

#view pivot table
print(df_pivot)

      assists  points
team                 
A          14      18
B          19      26
C          25      23

By default, pandas sorts the rows of the pivot table in alphabetical order based on the value in the index column, which happens to be the team column.

However, we can use the sort_values() function to instead sort the rows of the pivot table based on the values in the points column:

#sort pivot table by value in 'points' column in descending order
sorted_df_pivot = df_pivot.sort_values(by=['points'], ascending=False)

#view sorted pivot table
print(sorted_df_pivot)

      assists  points
team                 
B          19      26
C          25      23
A          14      18

Notice that the rows of the pivot table are now sorted based on the values in the points column.

Note that if you leave off the ascending=False argument, the rows will be sorted by the values in the points column in ascending order instead:

#sort pivot table by value in 'points' column in ascending order
sorted_df_pivot = df_pivot.sort_values(by=['points'])

#view sorted pivot table
print(sorted_df_pivot)

      assists  points
team                 
A          14      18
C          25      23
B          19      26

Notice that the rows in the pivot table are now sorted by the values in the points column in ascending (smallest to largest) order.

Note #1: You can also sort by multiple columns in the pivot table by passing multiple values to the by argument within the sort_values() function.

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