Altair Tutorial - Speaker Deck

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Exploratory Data Visualization with Altair

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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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Python Visualization Landscape

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Python Visualization Landscape

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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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Key: Visualization concepts should map directly to visualization implementation

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Hypothesis: Good implementation can influence good conceptualization

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~ familiar tools ~

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

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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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Problem: We’re mixing the what with the how

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

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Altair for Statistical Visualization

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Encodings are Flexible:

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Altair is Interactive

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...

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~ From D3 to Vega to Altair ~

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D3 is everywhere

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But working with D3 can be challenging ...

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“Simple” Barchart

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

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

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

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Altair is a Python API for creating Vega-Lite specifications

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~ Thinking about Visualization ~

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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/