Altair Tutorial

Here are the slides presented at an Altair tutorial and workshop I gave in the Department of Scientific Computing at FSU.

Transcript

1. Exploratory Data Visualization with Altair
3. Acknowledgments - Altair is developed by Jake Vanderplas and Brian Granger in close collaboration with the UW Interactive Data Lab - Much of this tutorial is from Jake Vanderplas’ PyCon 2018 tutorial - Special thanks to the Altair Community on GitHub.
4. Python Visualization Landscape
5. Python Visualization Landscape
6. Building Blocks of Visualization 1. Data 2. Transformations 3. Marks 4. Encoding - mapping from fields to mark properties 5. Scale - functions that map data to visual scales 6. Guides - visualizations of scales (axes, legends, etc.)
7. Key: Visualization concepts should map directly to visualization implementation
8. Hypothesis: Good implementation can influence good conceptualization
9. ~ familiar tools ~
12. Strengths: - Designed like MatLab: switching was easy - Many rendering backends - Can reproduce just about any plot (with a bit of effort) - Well-tested, standard tool for over a decade Weaknesses: - API is imperative & often overly verbose - Poor support for web/interactive graphics - Often slow for large & complicated data
13. Example: Statistical Data Tidy data: i.e. rows are samples, columns are features
14. Tidy data: i.e. rows are samples, columns are features “ I want to scatter petal length vs. sepal length, and color by species” Example: Statistical Data
17. Problem: We’re mixing the what with the how
18. Toward a well-motivated Declarative Visualization Imperative - Specify How something should be done. - Specification & Execution intertwined. - “Put a red circle here and a blue circle here” Declarative - Specify What should be done. - Separates Specification from Execution - “Map <x> to a position, and <y> to a color” Declarative visualizations lets you think about the data and relationships, rather than incidental details
19. Toward a well-motivated Declarative Visualization Imperative - Specify How something should be done. - Specification & Execution intertwined. - “Put a red circle here and a blue circle here” Declarative - Specify What should be done. - Separates Specification from Execution - “Map <x> to a position, and <y> to a color” Declarative visualizations lets you think about the data and relationships, rather than incidental details
20. Based on the Vega and Vega-Lite grammars https://altair-viz.github.io/ Altair Declarative visualization in Python
21. Altair for Statistical Visualization
22. Encodings are Flexible:
23. Altair is Interactive
24. ...
26. ~ From D3 to Vega to Altair ~
27. D3 is everywhere
28. But working with D3 can be challenging ...
29. “Simple” Barchart
30. D3 is a Javascript package that streamlines the manipulation of objects on a webpage
31. Vega is a detailed declarative specification for visualizations, built on D3
32. Vega-Lite is a simpler declarative specification aimed at statistical visualization
33. Altair is a Python API for creating Vega-Lite specifications
35. ~ Thinking about Visualization ~
45. Key: Visualization concepts should map directly to visualization implementation Check out Jeff Heer’s class on data visualization https://courses.cs.washington.edu/courses/cse442/17au/