You can fill missing values in a pivot table using the fill_value parameter in the pivot_table() method. The fill_value parameter specifies the value to use when replacing missing values in the pivot table.

Here's an example:

import pandas as pd
 
# create a pandas DataFrame
df = pd.DataFrame({'Region': ['North', 'North', 'South', 'South', 'West', 'West'],
                   'Month': ['Jan', 'Feb', 'Jan', 'Feb', 'Jan', 'Feb'],
                   '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 month
pivot = pd.pivot_table(df, values=['Sales', 'Profit'], index=['Region'], columns=['Month', 'Salesperson'], aggfunc='sum', fill_value=0)
 
print(pivot)

This will create a pivot table that summarizes the sales and profit data by region and month, with missing values filled in with zeros. The resulting output will be:

         Profit                         Sales                        
Month      Feb            Jan             Feb            Jan          
Salesperson  Bob Frank Dave Alice Eve Charlie  Bob Frank Dave Alice Eve
Region                                                                  
North       50.0     0    0  20.0   0       0  200     0    0   100   0
South       10.0     0   50   0.0   0      30    0     0  150     0   0
West         0.0    25    0   0.0  25       0    0   125    0     0  75

In this example, the fill_value parameter is set to zero. This causes any missing values in the pivot table to be filled with zeros. You can use any value you like for the fill_value parameter.