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Effortlessly Group by Python: A Step-by-Step Tutorial


Python GroupBy Tutorial: Your Guide to Grouping Data in Python

Whether you’ve just started working with pandas and want to master one of its core capabilities, or you’re looking to fill in some gaps in your understanding about .groupby(), this tutorial will help you to break down and visualize a pandas GroupBy operation from start to finish.

This tutorial is meant to complement the official pandas documentation and the pandas Cookbook, where you’ll see self-contained, bite-sized examples. Here, however, you’ll focus on three more involved walkthroughs that use real-world datasets.


Before you proceed, make sure that you have the latest version of pandas available within a new virtual environment. You can create a virtual environment and install pandas using the following commands:

Terminal window
# For Windows PowerShell
PS> python -m venv venv
PS> venv\Scripts\activate
(venv) PS> python -m pip install pandas
# For Linux + macOS Shell
$ python3 -m venv venv
$ source venv/bin/activate
(venv) $ python -m pip install pandas

Example 1: U.S. Congress Dataset

The U.S. Congress dataset contains public information on historical members of Congress and illustrates several fundamental capabilities of .groupby(). This example will show you:

  1. The Hello, World! of pandas GroupBy
  2. pandas GroupBy vs SQL
  3. How pandas GroupBy Works

Example 2: Air Quality Dataset

The air quality dataset contains periodic gas sensor readings. This example will allow you to work with floats and time series data. You will learn about:

  1. Grouping on Derived Arrays
  2. Resampling

Example 3: News Aggregator Dataset

The news aggregator dataset holds metadata on several hundred thousand news articles. You’ll be working with strings and doing text munging with .groupby(). This example covers:

  1. Using Lambda Functions in .groupby()
  2. Improving the Performance of .groupby()

pandas GroupBy: Putting It All Together

In this section, you’ll learn how to put all the concepts discussed in the previous examples together. You will see a step-by-step guide on how to perform a GroupBy operation on a real-world dataset.


In conclusion, this tutorial has provided you with a detailed understanding of how to use pandas GroupBy operations on real-world data. You have learned about the split-apply-combine chain of operations, how to decompose it into steps, and how to categorize methods of a pandas GroupBy object based on their intent and result.

Happy coding!