Python | Filling Missing Values in Pandas Pivot Tables: Strategies and Methods

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Filling missing values in pivot tables

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.