07-07, 08:00–12:00 (US/Pacific), Ballroom D
This tutorial is an introduction to data visualization using the popular Vega-Altair Python library. Vega-Altair provides a simple and expressive API, enabling authors to rapidly create a wide range of interactive charts.
Participants will explore the fundamentals of effective chart design and gain hands-on experience building a variety of visualizations using Vega-Altair's declarative API. Furthermore, this tutorial will introduce users to advanced topics such as data transformations and interaction design. We will finish off by covering practical workflows such as integrating Vega-Altair into dashboarding systems, publishing visualizations, and creating reusable, themed charting libraries. By the end of the session, attendees will have the skills to leverage Vega-Altair for both rapid prototyping and production-ready visualizations in diverse environments
This tutorial introduces attendees to Vega-Altair, a python library for creating beautiful and interactive charts. Over four hands-on sessions, we’ll explore everything from the basics of chart design to advanced techniques like interactivity and custom theming.
Part 1 focuses on the why behind great visualizations, covering principles like chart anatomy, perceptual efficiency, and common pitfalls, followed by a critique of famous Vega-Altair charts. Part 2 dives into the how, teaching participants to map data variables to visual properties using Vega-Altair’s API, with exercises to recreate and redesign charts. Part 3 introduces advanced topics like data transformations and interaction design. Finally, Part 4 covers practical workflows, including exporting charts, integrating with dashboarding tools, and creating custom charting libraries. Each part will include a mix of instruction and exercises.
By the end of this tutorial, participants will not only understand the theory behind great visualizations but also have the skills to create them. Participants will be equipped to design effective visualizations, apply advanced techniques like data transformations and interactivity, and integrate their work into real-world workflows and systems.
Familiarity with Jupyter Notebooks and Pandas is helpful.
No prior experience with data visualization is required.
