Python Polars: The Definitive Guide

Chapter 13. Summarizing and Aggregating

Summarizing and aggregating data is a crucial step to transforming raw datasets into meaningful insights. Whether you’re working with sales data, customer information, or sensor readings, the ability to group and aggregate your data allows you to answer important questions and identify trends that might otherwise be hidden.

You’ll often find yourself asking questions like:

• “What is the average sales revenue per store?”

• “How many unique products did each customer purchase?”

• “What is the total expenditure by product category each month?”

These are exactly the types of questions that aggregation helps you answer. By grouping your data based on one or more columns and then performing calculations—​such as sums, averages, or counts—​you can gain a clearer understanding of the underlying patterns in your dataset.

Polars makes this process simple and efficient with the df.group_by() method, which allows you to group your DataFrame by one or more columns and expressions. Once you’ve grouped the data, you can apply a variety of aggregation functions to summarize the results. For example, you can calculate the sum, mean, or median for each group, or count the number of rows in each group.

In this chapter, you’ll learn about:

• The GroupBy context and its available methods, and how to use them to analyze your data

• Working with grouping data based on temporal values using the methods df.group_by_dynamic(), df.rolling(), and Expr.over()