Altair Tutorial

Transcript

1. Exploratory Data Visualization with Altair

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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 ~

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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
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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

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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

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35. ~ Thinking about Visualization ~

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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/