Python's Visualization Landscape (PyCon 2017)

So you want to visualize some data in Python: which library do you choose? From Matplotlib to Seaborn to Bokeh to Plotly, Python has a range of mature tools to create beautiful visualizations, each with their own strengths and weaknesses. In this talk I’ll give an overview of the landscape of dataviz tools in Python, as well as some deeper dives into a few, so that you can intelligently choose which library to turn to for any given visualization task.

Video: https://www.youtube.com/watch?v=FytuB8nFHPQ

1. @jakevdp Jake VanderPlas Jake VanderPlas @jakevdp #PyCon2017 Python’s Visualization Landscape
2. @jakevdp Jake VanderPlas [Python’s Visualization Landscape] From the abstract: “In this talk I’ll give an overview of the landscape of dataviz tools in Python . . .”
3. @jakevdp Jake VanderPlas [Python’s Visualization Landscape] From the abstract: “In this talk I’ll give an overview of the landscape of dataviz tools in Python . . .”
4. @jakevdp Jake VanderPlas From the abstract: “In this talk I’ll give an overview of the landscape of dataviz tools in Python . . .” [Python’s Visualization Landscape]
5. @jakevdp Jake VanderPlas From the abstract: “In this talk I’ll give an overview of the landscape of dataviz tools in Python . . .” [Python’s Visualization Landscape]
6. @jakevdp Jake VanderPlas From the abstract: “In this talk I’ll give an overview of the landscape of dataviz tools in Python . . .” [Python’s Visualization Landscape]
7. @jakevdp Jake VanderPlas From the abstract: “In this talk I’ll give an overview of the landscape of dataviz tools in Python . . .” [Python’s Visualization Landscape]
8. @jakevdp Jake VanderPlas From the abstract: “In this talk I’ll give an overview of the landscape of dataviz tools in Python . . .” [Python’s Visualization Landscape]
9. @jakevdp Jake VanderPlas From the abstract: “In this talk I’ll give an overview of the landscape of dataviz tools in Python . . .” [Python’s Visualization Landscape] From the abstract: “In this talk I’ll give an overview of the landscape of dataviz tools in Python . . .”
10. @jakevdp Jake VanderPlas [Python’s Visualization Landscape] From the abstract: “In this talk I’ll give an overview of the landscape of dataviz tools in Python . . .”
11. @jakevdp Jake VanderPlas [Making Sense of the Deluge]
12. @jakevdp Jake VanderPlas matplotlib
13. @jakevdp Jake VanderPlas matplotlib basemap/ cartopy
14. @jakevdp Jake VanderPlas matplotlib seaborn pandas basemap/ cartopy
15. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy networkx basemap/ cartopy
16. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap/ cartopy
17. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy
18. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy
19. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy javascript
20. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy javascript bokeh plotly
21. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy javascript bqplot bokeh toyplot plotly
22. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy javascript pythreejs bqplot bokeh toyplot plotly ipyvolume ipyleaflet
23. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy javascript pythreejs bqplot bokeh toyplot plotly ipyvolume cufflinks ipyleaflet
24. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy javascript pythreejs bqplot bokeh toyplot plotly ipyvolume cufflinks ipyleaflet
25. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy javascript pythreejs bqplot bokeh toyplot plotly ipyvolume cufflinks d3js mpld3 ipyleaflet
26. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy javascript pythreejs bqplot bokeh toyplot plotly ipyvolume cufflinks d3js mpld3 ipyleaflet Vega-Lite Vega
27. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy javascript pythreejs bqplot bokeh toyplot plotly ipyvolume cufflinks d3js mpld3 Altair Vincent ipyleaflet d3po Vega-Lite Vega
28. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy javascript pythreejs bqplot bokeh toyplot plotly ipyvolume cufflinks d3js mpld3 Altair Vincent ipyleaflet d3po Vega-Lite Vega
29. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy javascript pythreejs bqplot bokeh toyplot plotly ipyvolume cufflinks datashader d3js mpld3 Altair Vincent ipyleaflet d3po Vega-Lite Vega
30. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy javascript pythreejs bqplot bokeh toyplot plotly ipyvolume cufflinks datashader d3js mpld3 Altair Vincent ipyleaflet d3po Vega-Lite Vega Vaex
31. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy javascript pythreejs bqplot bokeh toyplot plotly ipyvolume cufflinks holoviews datashader d3js mpld3 Altair Vincent ipyleaflet d3po Vega-Lite Vega Vaex
32. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy javascript pythreejs bqplot bokeh toyplot plotly ipyvolume cufflinks holoviews datashader d3js mpld3 Altair Vincent OpenGL ipyleaflet d3po Vega-Lite Vega Vaex
33. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy javascript pythreejs bqplot bokeh toyplot plotly ipyvolume cufflinks holoviews datashader d3js mpld3 Altair Vincent OpenGL Glumpy Vispy ipyleaflet d3po Vega-Lite Vega Vaex
34. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy javascript pythreejs bqplot bokeh toyplot plotly ipyvolume cufflinks holoviews datashader d3js mpld3 Altair Vincent OpenGL Glumpy Vispy ipyleaflet d3po Vega-Lite Vega graphviz Vaex graph-tool
35. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy javascript pythreejs bqplot bokeh toyplot plotly ipyvolume cufflinks holoviews datashader d3js mpld3 Altair Vincent OpenGL Glumpy Vispy ipyleaflet Lightning GlueViz YT d3po Vega-Lite Vega MayaVi graphviz GR framework PyQTgraph pygal chaco Vaex graph-tool
36. @jakevdp Jake VanderPlas Python’s Visualization Landscape matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy javascript pythreejs bqplot bokeh toyplot plotly ipyvolume cufflinks holoviews datashader d3js mpld3 Altair Vincent OpenGL Glumpy Vispy ipyleaflet Lightning GlueViz YT d3po Vega-Lite Vega MayaVi graphviz GR framework PyQTgraph pygal chaco Vaex graph-tool
37. @jakevdp Jake VanderPlas
38. @jakevdp Jake VanderPlas How did we get here?
39. @jakevdp Jake VanderPlas In the beginning was matplotlib* * well, actually… Python visualization existed before matplotlib, but was not very mature.
40. @jakevdp Jake VanderPlas Plotting with Matplotlib Strengths: - Designed like MatLab: switching was easy
41. @jakevdp Jake VanderPlas Plotting with Matplotlib Strengths: - Designed like MatLab: switching was easy - Many rendering backends
42. @jakevdp Jake VanderPlas Plotting with Matplotlib Strengths: - Designed like MatLab: switching was easy - Many rendering backends - Can reproduce just about any plot (with a bit of effort)
43. @jakevdp Jake VanderPlas Plotting with Matplotlib 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
44. @jakevdp Jake VanderPlas Matplotlib Gallery
45. @jakevdp Jake VanderPlas import pandas as pd iris = pd.read_csv('iris.csv') iris.head() Tidy data: i.e. rows are samples, columns are features Example: Statistical Data
46. @jakevdp Jake VanderPlas “I want to scatter petal length vs. sepal length, and color by species” Just a simple visualization . . .
47. @jakevdp Jake VanderPlas color_map = dict(zip(iris.species.unique(), ['blue', 'green', 'red'])) for species, group in iris.groupby('species'): plt.scatter(group['petalLength'], group['sepalLength'], color=color_map[species], alpha=0.3, edgecolor=None, label=species) plt.legend(frameon=True, title='species') plt.xlabel('petalLength') plt.ylabel('sepalLength') Just a simple visualization . . .
48. @jakevdp Jake VanderPlas Plotting with Matplotlib 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 - Sometimes poor stylistic defaults - Poor support for web/interactive graphs - Often slow for large & complicated data
49. @jakevdp Jake VanderPlas Everyone’s Goal: Improve on the weaknesses of matplotlib (without sacrificing the strengths!)
50. @jakevdp Jake VanderPlas Building on Matplotlib. . . matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy
51. @jakevdp Jake VanderPlas Building on Matplotlib. . . Common Idea: Keep matplotlib as a versatile, well-tested backend, and provide a new domain-specific API. matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy
52. @jakevdp Jake VanderPlas Building on Matplotlib. . . matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy
53. @jakevdp Jake VanderPlas Pandas plotting API Key Features: - Pandas provides a DataFrame object - Also provides a simple API for plotting DataFrames
54. @jakevdp Jake VanderPlas iris.plot.scatter('petalLength', 'petalWidth')
55. @jakevdp Jake VanderPlas from pandas.tools.plotting import andrews_curves andrews_curves(iris, 'species') - More sophisticated statistical visualization tools have recently been added
56. @jakevdp Jake VanderPlas http://seaborn.pydata.org Key Features: - Like Pandas, wraps matplotlib - Nice set of color palettes & plot styles - Focus on statistical visualization & modeling Seaborn: statistical data visualization
57. @jakevdp Jake VanderPlas import seaborn as sns sns.lmplot('petalLength', 'sepalWidth', iris, hue='species', fit_reg=False) Seaborn examples
58. @jakevdp Jake VanderPlas sns.pairplot(iris, hue='species') Seaborn examples
59. @jakevdp Jake VanderPlas Javascript-based Viz: javascript pythreejs bqplot bokeh toyplot plotly ipyvolume cufflinks ipyleaflet
60. @jakevdp Jake VanderPlas Javascript-based Viz: Common Idea: build a new API that produces a plot serialization (often JSON) that can be displayed in the browser (often in Jupyter notebooks) javascript pythreejs bqplot bokeh toyplot plotly ipyvolume cufflinks ipyleaflet
61. @jakevdp Jake VanderPlas Javascript-based Viz: javascript pythreejs bqplot toyplot ipyvolume cufflinks ipyleaflet bokeh plotly
62. @jakevdp Jake VanderPlas Plotting with Bokeh
63. @jakevdp Jake VanderPlas Bokeh Gallery
64. @jakevdp Jake VanderPlas Plotting with Bokeh Advantages: - Web view/interactivity - Imperative and Declarative layer - Handles large and/or streaming datasets - Geographical visualization - Fully open source Disadvantages: - No vector output (need PDF/EPS? Sorry) - Newer tool with a smaller user-base than matplotlib
65. @jakevdp Jake VanderPlas Basic Plotting with Plotly
66. @jakevdp Jake VanderPlas Plotly Gallery
67. @jakevdp Jake VanderPlas Plotting with Plotly Advantages: - Web view/interactivity - Multi-language support - 3D plotting capability - Animation capability - Geographical visualization Disadvantages: - Some features require a paid plan
68. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy javascript pythreejs bqplot bokeh toyplot plotly ipyvolume cufflinks holoviews datashader d3js mpld3 Altair Vincent OpenGL Glumpy Vispy ipyleaflet Lightning GlueViz YT d3po Vega-Lite Vega MayaVi graphviz GR framework PyQTgraph pygal chaco Vaex graph-tool Visualization for Larger Data . . .
69. @jakevdp Jake VanderPlas matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy javascript pythreejs bqplot bokeh toyplot plotly ipyvolume cufflinks holoviews d3js mpld3 Altair Vincent OpenGL Glumpy Vispy ipyleaflet Lightning GlueViz YT d3po Vega-Lite Vega MayaVi graphviz GR framework PyQTgraph pygal chaco Vaex graph-tool Visualization for Larger Data . . . datashader
70. @jakevdp Jake VanderPlas Datashader Fast server-side engine for dynamic data aggregation
71. @jakevdp Jake VanderPlas Datashader - Compute layer that works with Bokeh - Rather than sending data to the client, it aggregates data and sends pixels. - Can handle interactive visualization of billions of rows.
72. @jakevdp Jake VanderPlas Datashader - Compute layer that works with Bokeh - Rather than sending data to the client, it aggregates data and sends pixels. - Can handle interactive visualization of billions of rows.
73. @jakevdp Jake VanderPlas seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy pythreejs bqplot toyplot plotly ipyvolume cufflinks holoviews datashader mpld3 Vincent OpenGL Glumpy Vispy ipyleaflet Lightning GlueViz YT d3po Vega-Lite Vega MayaVi graphviz GR framework PyQTgraph pygal chaco Vaex graph-tool Toward Declarative Visualization . . . d3js javascript bokeh matplotlib Altair
74. @jakevdp Jake VanderPlas seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy pythreejs bqplot toyplot plotly ipyvolume cufflinks holoviews mpld3 Vincent OpenGL Glumpy Vispy ipyleaflet Lightning GlueViz YT d3po Vega-Lite Vega MayaVi graphviz GR framework PyQTgraph pygal chaco Vaex graph-tool Toward Declarative Visualization . . . d3js javascript bokeh matplotlib Altair datashader
75. @jakevdp Jake VanderPlas Holoviews - Datasets themselves stored in objects that automatically produce intelligent visualizations - Composition & Interactivity via operator overloading - Renders to Bokeh, DataShader, and Matplotlib
76. @jakevdp Jake VanderPlas Holoviews - Also can handle geographic data & time-series
77. @jakevdp Jake VanderPlas What if instead of passing around pixels, we pass around visualization specifications plus data? Altair
78. @jakevdp Jake VanderPlas What if instead of passing around pixels, we pass around visualization specifications plus data? “Declarative Visualization” Altair
79. @jakevdp Jake VanderPlas What if instead of passing around pixels, we pass around visualization specifications plus data? “Declarative Visualization” Altair
80. @jakevdp Jake VanderPlas Declarative Visualization: Viz for data science 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.
81. #JSM2016 Jake VanderPlas From D3 to Altair . . . (link to live version)
82. #JSM2016 Jake VanderPlas But working in D3 can be challenging . . .
83. #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.
84. #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.
85. #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.
86. #JSM2016 Jake VanderPlas Bar Chart: Altair Altair is a Python API for creating Vega-Lite specifications.
87. @jakevdp Jake VanderPlas From Declarative API to declarative Grammar chart = Chart(data).mark_circle( opacity=0.3 ).encode( x='petalLength:Q', y='sepalWidth:Q', color='species:N', ) chart.display()
88. @jakevdp Jake VanderPlas From Declarative API to declarative Grammar >>> chart.to_dict() {'config': {'mark': {'opacity': 0.3}}, 'data': {'url': 'https://vega.github.io/vega-datasets/data/iris.json'}, 'encoding': {'color': {'field': 'species', 'type': 'nominal'}, 'x': {'field': 'petalLength', 'type': 'quantitative'}, 'y': {'field': 'sepalWidth', 'type': 'quantitative'}}, 'mark': 'circle'}
89. #JSM2016 Jake VanderPlas (Visualizations from jakevdp/altair-examples).
90. #JSM2016 Jake VanderPlas Coming Very Soon: Altair 2.0 - Includes a Grammar of Interaction
91. @jakevdp Jake VanderPlas or $ conda install altair --channel conda-forge $ pip install altair $ jupyter nbextension install --sys-prefix --py vega Try Altair: http://github.com/ellisonbg/altair/ For a Jupyter notebook tutorial, type import altair altair.tutorial()
92. @jakevdp Jake VanderPlas Python’s Visualization Landscape matplotlib seaborn pandas ggpy scikit- plot Yellow brick networkx basemap /cartopy javascript pythreejs bqplot bokeh toyplot plotly ipyvolume cufflinks holoviews datashader d3js mpld3 Altair Vincent OpenGL Glumpy Vispy ipyleaflet Lightning GlueViz YT d3po Vega-Lite Vega MayaVi graphviz GR framework PyQTgraph pygal chaco Vaex graph-tool
93. @jakevdp Jake VanderPlas Email: jakevdp@uw.edu Twitter: @jakevdp Github: jakevdp Web: http://vanderplas.com/ Blog: http://jakevdp.github.io/ Thank You!