Altair: Declarative Visualization in Python (DSE Summit 2016)

In this lightning talk, I give a quick tour and motivation for Altair, the Python library for declarative statistical visualization based on Vega-Lite. http://altair-viz.github.io/

Transcript

1. #JSM2016 Jake VanderPlas Altair: Declarative Visualization in Python Jake VanderPlas @jakevdp Oct 26, 2016
2. #JSM2016 Jake VanderPlas D3 is Everywhere . . . (click for live version)
3. #JSM2016 Jake VanderPlas But working in D3 can be challenging . . .
4. #JSM2016 Jake VanderPlas Bar Chart: d3 var margin = {top: 20, right: 20, bottom: 30, left: 40}, width = 960 - margin.left - margin.right, height = 500 - margin.top - margin.bottom; var x = d3.scale.ordinal() .rangeRoundBands([0, width], .1); var y = d3.scale.linear() .range([height, 0]); var xAxis = d3.svg.axis() .scale(x) .orient("bottom"); var yAxis = d3.svg.axis() .scale(y) .orient("left") .ticks(10, "%"); var svg = d3.select("body").append("svg") .attr("width", width + margin.left + margin.right) .attr("height", height + margin.top + margin.bottom) .append("g") .attr("transform", "translate(" + margin.left + "," + margin.top + ")"); d3.tsv("data.tsv", type, function(error, data) { if (error) throw error; x.domain(data.map(function(d) { return d.letter; })); y.domain([0, d3.max(data, function(d) { return d.frequency; })]); svg.append("g") .attr("class", "x axis") .attr("transform", "translate(0," + height + ")") .call(xAxis); svg.append("g") .attr("class", "y axis") .call(yAxis) .append("text") .attr("transform", "rotate(-90)") .attr("y", 6) .attr("dy", ".71em") .style("text-anchor", "end") .text("Frequency"); svg.selectAll(".bar") .data(data) .enter().append("rect") .attr("class", "bar") .attr("x", function(d) { return x(d.letter); }) .attr("width", x.rangeBand()) .attr("y", function(d) { return y(d.frequency); }) .attr("height", function(d) { return height - y(d.frequency); }); }); function type(d) { d.frequency = +d.frequency; return d; } D3 is a Javascript package that streamlines manipulation of objects on a webpage.
5. #JSM2016 Jake VanderPlas Bar Chart: Vega { "width": 400, "height": 200, "padding": {"top": 10, "left": 30, "bottom": 30, "right": 10}, "data": [ { "name": "table", "values": [ {"x": 1, "y": 28}, {"x": 2, "y": 55}, {"x": 3, "y": 43}, {"x": 4, "y": 91}, {"x": 5, "y": 81}, {"x": 6, "y": 53}, {"x": 7, "y": 19}, {"x": 8, "y": 87}, {"x": 9, "y": 52}, {"x": 10, "y": 48}, {"x": 11, "y": 24}, {"x": 12, "y": 49}, {"x": 13, "y": 87}, {"x": 14, "y": 66}, {"x": 15, "y": 17}, {"x": 16, "y": 27}, {"x": 17, "y": 68}, {"x": 18, "y": 16}, {"x": 19, "y": 49}, {"x": 20, "y": 15} ] } ], "scales": [ { "name": "x", "type": "ordinal", "range": "width", "domain": {"data": "table", "field": "x"} }, { "name": "y", "type": "linear", "range": "height", "domain": {"data": "table", "field": "y"}, "nice": true } ], "axes": [ {"type": "x", "scale": "x"}, {"type": "y", "scale": "y"} ], "marks": [ { "type": "rect", "from": {"data": "table"}, "properties": { "enter": { "x": {"scale": "x", "field": "x"}, "width": {"scale": "x", "band": true, "offset": -1}, "y": {"scale": "y", "field": "y"}, "y2": {"scale": "y", "value": 0} }, "update": { "fill": {"value": "steelblue"} Vega is a detailed declarative specification for visualizations, built on D3.
6. #JSM2016 Jake VanderPlas Bar Chart: Vega-Lite { "description": "A simple bar chart with embedded data.", "data": { "values": [ {"a": "A","b": 28}, {"a": "B","b": 55}, {"a": "C","b": 43}, {"a": "D","b": 91}, {"a": "E","b": 81}, {"a": "F","b": 53}, {"a": "G","b": 19}, {"a": "H","b": 87}, {"a": "I","b": 52} ] }, "mark": "bar", "encoding": { "x": {"field": "a", "type": "ordinal"}, "y": {"field": "b", "type": "quantitative"} } } Vega-Lite is a simpler declarative specification aimed at statistical visualization.
7. #JSM2016 Jake VanderPlas Bar Chart: Altair Altair is a Python API for creating Vega-Lite specifications.
8. #JSM2016 Jake VanderPlas Altair Declarative statistical visualization library for Python, driven by Vega-Lite http://altair-viz.github.io/ Collaboration between Brian Granger (Jupyter team), myself, and UW’s Interactive Data Lab
9. #JSM2016 Jake VanderPlas Example: Cars Dataset Altair works seamlessly with Pandas dataframes, a standard data format in Python
10. #JSM2016 Jake VanderPlas Example: Cars Dataset Specify mappings from visual components to data columns
11. #JSM2016 Jake VanderPlas Key feature: Altair provides a Declarative API Declarative - Specify What should be done - Details determined automatically - Separates Specification from Execution Imperative - Specify How something should be done. - Must manually specify plotting steps - Specification & Execution intertwined. Declarative visualization lets you think about data and relationships, rather than incidental details.
12. #JSM2016 Jake VanderPlas Matplotlib is an imperative API: Specify details of how to build the visualization
13. #JSM2016 Jake VanderPlas Altair is a declarative API: Specify what quantities should be mapped to each visual encoding
14. #JSM2016 Jake VanderPlas But why another plotting library? Teaching: students can learn visualization concepts with minimal syntactic distraction. Publishing: Instead of publishing pixels, can publish data + plot specification for greater flexibility & reproducibility. Cross-Pollenation: Vega-Lite has the potential to provide a cross-platform lingua franca of statistical visualization. - Matplotlib - Bokeh - Plotly - Seaborn - Holoviews - VisPy - ggplot - pandas plot - Lightning
15. #JSM2016 Jake VanderPlas Altair/Vega-Lite supports many plot types:
16. #JSM2016 Jake VanderPlas Altair/Vega-Lite supports many plot types:
17. #JSM2016 Jake VanderPlas Altair/Vega-Lite supports many plot types:
18. #JSM2016 Jake VanderPlas Altair/Vega-Lite supports many plot types:
19. #JSM2016 Jake VanderPlas Altair/Vega-Lite supports many plot types:
20. #JSM2016 Jake VanderPlas Altair/Vega-Lite supports many plot types:
21. #JSM2016 Jake VanderPlas (Visualizations from jakevdp/altair-examples).
22. #JSM2016 Jake VanderPlas or $ conda install altair --channel conda-forge $ pip install altair $ jupyter nbextension install --sys-prefix --py vega Try Altair: Website: http://altair-viz.github.io/ For a Jupyter notebook tutorial, type import altair altair.tutorial()
23. #JSM2016 Jake VanderPlas Email: jakevdp@uw.edu Twitter: @jakevdp Github: jakevdp Web: http://vanderplas.com Blog: http://jakevdp.github.io Thank You!
24. #JSM2016 Jake VanderPlas
25. #JSM2016 Jake VanderPlas Altair is a declarative API: Altair’s creates validated Vega-Lite specifications: - Portable JSON serialization (Vega-Lite spec) - Interest from other viz libraries (matplotlib, Bokeh, Plotly) in supporting this serialization. - Potential for cross-language compatibility
26. #JSM2016 Jake VanderPlas Vega-Lite schema is well-defined; allows round-trip between spec and code: