Pandas Pivot Tables: Complete Guide with Examples (2026)

What Are Pivot Tables? If you have ever used Excel or Google Sheets, you have probably encountered pivot tables. They are one of the most powerful features in any spreadsheet application -- the ability to take a flat table of data and reshape it into a summary view that groups, aggregates, and cross-tabulates your data in a single operation. Pandas brings this same concept to Python with pivot_table(). Instead of dragging fields into "Rows" and "Columns" areas in a GUI, you pass parameters to a function. The result is a DataFrame that summarizes your raw data exactly the way you need it -- total sales by region and product, average response time by department and quarter, count of support tickets by priority and status. When to use pivot tables vs other methods Pivot tables are the right choice when you need a cross-tabulated summary -- meaning you want categories along both rows and columns. Here is a quick decision guide: Pivot table: You want to see sales broken down by region (rows