Real Python: pandas GroupBy: Your Guide to Grouping Data in Python

The pandas .groupby() method is used to analyze and transform datasets in Python by splitting a DataFrame into groups based on column values, applying functions to each group, and combining the results into a new DataFrame. This technique is essential for tasks like aggregation, filtering, and transformation on grouped data. The .groupby() method can be used to group by a single column or multiple columns by passing a list of column names. Common aggregation methods in pandas include .sum(), .mean(), and .count(). Custom functions can also be used with pandas .groupby() to perform specific operations on groups. This tutorial assumes prior experience with pandas and provides datasets for practice, including the U.S. Congress dataset, air quality dataset, and news aggregator dataset. To follow along, ensure you have the latest version of pandas installed in a new virtual environment and download the datasets. The datasets can be downloaded as a .zip file and unzipped to a folder called groupby-data/ in your current directory. The tutorial will use these datasets to demonstrate the capabilities of .groupby(). The first example uses the U.S. Congress dataset, which contains public information on historical members of Congress, and demonstrates how to read the CSV file into a pandas DataFrame using read_csv().