You can calculate different statistics in a pivot table by passing a different aggregation function to the aggfunc parameter in the pivot_table() method. Here's an example:

import pandas as pd
 
# create a pandas DataFrame
df = pd.DataFrame({'Region': ['North', 'North', 'South', 'South', 'West', 'West'],
                   'Salesperson': ['Alice', 'Bob', 'Charlie', 'Dave', 'Eve', 'Frank'],
                   'Sales': [100, 200, 150, 50, 75, 125],
                   'Profit': [20, 50, 30, 10, 15, 25]})
 
# create a pivot table that summarizes the sales and profit data by region and salesperson
pivot = pd.pivot_table(df, values=['Sales', 'Profit'], index=['Region'], columns=['Salesperson'], aggfunc={'Sales': sum, 'Profit': 'mean'})
 
print(pivot)

This will create a pivot table that summarizes the sales and profit data by region and salesperson. The resulting output will be:

            Profit                                  Sales                      
Salesperson  Alice   Bob Charlie  Dave   Eve Frank  Alice  Bob Charlie Dave  Eve Frank
Region                                                                                
North           20  50.0     NaN   NaN   NaN   NaN    100  200     NaN  NaN  NaN   NaN
South          NaN   NaN    30.0  10.0   NaN   NaN    NaN  NaN   150.0  50.0  NaN   NaN
West           NaN   NaN     NaN   NaN  15.0  25.0    NaN  NaN     NaN  NaN  75.0  125.0

The resulting pivot table shows the mean profit for each salesperson broken down by region, as well as the total sales for each salesperson broken down by region. The aggfunc parameter is passed a dictionary that specifies the aggregation functions to use for each column. In this case, we're using the sum function to summarize the sales data, and the 'mean' string to summarize the profit data. You can pass any function that can be used to summarize data, including built-in functions such as sum(), mean(), max(), min(), and custom functions.