Visualization in Python with Altair
While Python is my preferred programming language for scripted data transformations, I have avoided routinely doing visualization in Python. I could follow examples for the many Python visualization libraries, but in the end they all seemed confusing and made it hard to do the types of exploratory visualization that Tableau made easy. Finally, Altair has emerged as a viable alternative for me, with the way it thinks about data and the visualization process, and I thought others might be interested in learning more about it.
Altair is a declarative statistical visualization library for Python, built on top of the well-design and powerful Vega-Lite visualization grammar. (Vega-lite was built for the web, includes interaction, and is being adopted as a standard by high profile websites and tools.) It works well for small to medium-sized tabular data (like spreadsheets).
In this workshop, I’ll run you through both some introductory and more complex examples using Altair with Python in Jupyter notebooks, so you can get a feeling for how you might use it in your own work. No programming experience is required, but you’ll probably be more comfortable following along if you have some previous exposure to Python, since I won’t be spending time on the language itself.
- Altair web site: https://altair-viz.github.io/
- Blog post, “Why I’m backing Vega-Lite as our default tool for data visualization”: https://email@example.com/why-im-backing-vega-lite-as-our-default-tool-for-data-visualisation-51c20970df39
The content of the workshop may be recorded. Cameras will capture the instructor station and the display screen. Ambient audio, including verbal audience participation, will be included on the recording. Registration for the workshop indicates consent. If you are uncomfortable with a recording being published please contact the instructor at anytime prior to the conclusion of the workshop.
- Tuesday, February 19, 2019
- 10:00am - 12:00pm
- Bostock 023 Training Room
- West Campus
- Data and Visualization