Seaborn in Python Using English Premier League Data

Using English Premier League data to introduce Seaborn visualizations in Python

Author

Affiliation

Austin Hayes

University of North Carolina at Charlotte

NoteFacilitation notes

This module was originally developed as a Jupyter notebook. The SCORE preprint server does not run the notebook directly, so users should download the materials below and run the notebook locally or in a Jupyter-compatible environment.

Students should be provided with the following files:

• Student-facing Jupyter notebook
• English Premier League player statistics data
• La Liga player statistics data

Additional instructor materials are available below:

• Answer key Jupyter notebook

Module Files

The full module materials are linked below.

• Student-facing Jupyter notebook
• Answer key Jupyter notebook
• English Premier League player statistics data
• La Liga player statistics data

Module

This shell page provides access to the module materials. Download the student-facing Jupyter notebook and the supporting data files above to complete the activity.

To run the notebook, users will need a Python environment with packages such as pandas, numpy, seaborn, and matplotlib.

Summary

In this module, students use English Premier League data to learn how to create and interpret visualizations in Python using the Seaborn library.