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.
   

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4. Python Visualization Landscape
   

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5. Python Visualization Landscape
   

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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.)
   

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7. Key: Visualization concepts should map
   directly to visualization implementation
   

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8. Hypothesis: Good implementation can
   influence good conceptualization
   

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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
    

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13. Example: Statistical Data
    Tidy data: i.e. rows are samples, columns are features
    

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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
    

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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 to a position,
    and to a color”
    Declarative visualizations lets you think about the data
    and relationships, rather than incidental details
    

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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 to a position,
    and to a color”
    Declarative visualizations lets you think about the data
    and relationships, rather than incidental details
    

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20. Based on the Vega and Vega-Lite grammars
    https://altair-viz.github.io/
    Altair
    Declarative visualization in Python
    

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21. Altair for Statistical Visualization
    

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22. Encodings are Flexible:
    

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23. Altair is Interactive
    

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24. ...
    

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26. ~ From D3 to Vega to Altair
    ~
    

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27. D3 is everywhere
    

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28. But working with D3 can
    be challenging ...
    

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29. “Simple”
    Barchart
    

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30. D3 is a Javascript package
    that streamlines the
    manipulation of objects on
    a webpage
    

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31. Vega is a detailed
    declarative specification
    for visualizations, built on
    D3
    

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32. Vega-Lite is a simpler
    declarative specification
    aimed at statistical
    visualization
    

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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/
    

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