Python is a very popular language for computational and data science, and it has many powerful capabilities for visualizing data in the pursuit of understanding and conveying underlying patterns. The first part of this series will showcase various ways that Python can be used for creating graphics, following which we’ll dive into some common Python libraries for data viz: Matplotlib, Pandas, and Seaborn. Along the way we’ll cover how these libraries fit together — Matplotlib’s underlying figure-and-axes model, Pandas’ convenient one-line plotting for quick exploration, and Seaborn’s higher-level interface for statistical graphics — as well as the practical details that make a figure publication-ready. We will go through several interactive exercises so that attendees can gain direct experience in using these libraries and will be prepared to learn more on their own after the workshop. Basic familiarity with Python will be useful.