Python | Pivot Tables in Pandas: How to Transform Your Data and Unleash Its Full Potential in Python

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Pivot tables

A pivot table is a powerful data analysis tool in Python that allows you to summarize and group data based on one or more columns. It's similar to the pivot table function in spreadsheet applications such as Microsoft Excel.

To create a pivot table in Python, you can use the pivot_table() method in pandas. 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]})

# create a pivot table that summarizes the sales data by region and salesperson

pivot = pd.pivot_table(df, values='Sales', index=['Region'], columns=['Salesperson'], aggfunc=sum)

print(pivot)

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

Salesperson Alice Bob Charlie Dave Eve Frank

Region

North 100 200 NaN NaN NaN NaN

South NaN NaN 150.0 50.0 NaN NaN

West NaN NaN NaN NaN 75.0 125.0

The resulting pivot table shows the total sales for each salesperson broken down by region. The NaN values indicate that there were no sales made by a particular salesperson in a particular region. You can change the aggregation function used to summarize the data by passing a different function to the aggfunc parameter.

You can also add additional columns to the pivot table by passing their names to the values parameter, and additional grouping variables to the index and columns parameters.