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Visualization in Python with Altair

Visualization in Python with Altair

Introducing Altair for declarative statistical visualization in Python. Talk given at the Puget Sound Python meetup, Nov 9, 2016

Jake VanderPlas

November 09, 2016
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  1. @jakevdp
    Jake VanderPlas
    Jake VanderPlas @jakevdp
    Puget Sound Python
    Nov 9, 2016
    Visualization in Python
    with Altair

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  2. @jakevdp
    Jake VanderPlas
    Statistical
    Visualization in Python
    with Altair
    Jake VanderPlas @jakevdp
    Puget Sound Python
    Nov 9, 2016

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  3. @jakevdp
    Jake VanderPlas
    Declarative Statistical
    Visualization in Python
    with Altair
    Jake VanderPlas @jakevdp
    Puget Sound Python
    Nov 9, 2016

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  4. @jakevdp
    Jake VanderPlas
    Python Viz is a bit Painful...
    "I have been using Matplotlib for a decade
    now, and I still have to look most things up"
    “I love Python but I switch to R for
    making plots”
    “I do viz in Python, but switch from
    matplotlib to seaborn to bokeh
    depending on what I need to do”

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  5. @jakevdp
    Jake VanderPlas
    Problem: where would you tell
    beginners to start?
    - Matplotlib
    - Bokeh
    - Plotly
    - Seaborn
    - Holoviews
    - VisPy
    - ggplot
    - pandas plot
    - Lightning
    Each library has strengths, but
    arguably none is yet the “killer
    viz app” for Data Science.

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  6. @jakevdp
    Jake VanderPlas
    Some examples . . .

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  7. @jakevdp
    Jake VanderPlas
    import matplotlib.pyplot as plt
    from numpy.random import rand
    for color in ['red', 'green', 'blue']:
    x, y = rand(2, 100)
    size = 200.0 * rand(100)
    plt.scatter(x, y, c=color, s=size, label=color,
    alpha=0.3, edgecolor='none')
    plt.legend(frameon=True)
    plt.show()
    Plotting with Matplotlib

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  8. @jakevdp
    Jake VanderPlas
    Plotting with Matplotlib
    Advantages:
    - Matlab-like API
    - Well-tested, standard tool for over a decade
    - LOADS of rendering backends
    - Can reproduce just about any plot… if you have time
    Disadvantages:
    - Matlab-like API
    - Often poor stylistic defaults (though see 2.0 release)
    - Imperative model: lots of manual tweaking required
    (though see Seaborn & ggplot)
    - Poor support for web/interactive graphs
    (though see http://mpld3.github.io/)
    - Often slow for large & complicated data

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  9. @jakevdp
    Jake VanderPlas
    Matplotlib Gallery

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  10. @jakevdp
    Jake VanderPlas
    from bokeh.plotting import figure, show
    from bokeh.models import LinearAxis, Range1d
    p = figure()
    for color in ['red', 'green', 'blue']:
    x, y = rand(2, 100)
    size = 0.03 * rand(100)
    p.circle(x, y, fill_color=color, radius=size,
    legend=color, fill_alpha=0.3,
    line_color=None)
    show(p)
    Plotting with Bokeh

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  11. @jakevdp
    Jake VanderPlas
    Plotting with Bokeh
    Advantages:
    - Web view/interactivity
    - Imperative and Declarative layer
    - Handles large and/or streaming datasets
    - Modern default plot styles
    Disadvantages:
    - No vector output (need PDF/EPS? Sorry)
    - Newer tool with a smaller user-base than
    matplotlib

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  12. @jakevdp
    Jake VanderPlas
    Bokeh Gallery

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  13. @jakevdp
    Jake VanderPlas
    Moving to Statistical
    Visualization

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  14. @jakevdp
    Jake VanderPlas
    from altair import load_dataset
    iris = load_dataset('iris')
    iris.head()
    Data in Tidy Format: i.e. rows are samples, columns are
    features
    Statistical Visualization

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  15. @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['sepalWidth'],
    color=color_map[species],
    alpha=0.3, edgecolor=None,
    label=species)
    plt.legend(frameon=True, title='species')
    plt.xlabel('petalLength')
    plt.ylabel('sepalLength')
    Statistical Visualization: Grouping

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  16. @jakevdp
    Jake VanderPlas
    color_map = dict(zip(iris.species.unique(),['blue', 'green', 'red']))
    n_panels = len(color_map)
    fig, ax = plt.subplots(1, n_panels, figsize=(n_panels * 5, 3),
    sharex
    =True, sharey=True)
    for i, (species, group) in enumerate(iris.groupby('species')):
    ax[i].scatter(group['petalLength'], group['sepalWidth'],
    color
    =color_map[species],
    alpha
    =0.3, edgecolor=None,
    label
    =species)
    ax[i].legend(frameon=True, title='species')
    plt.xlabel('petalLength')
    plt.ylabel('sepalLength')
    Statistical Visualization: Faceting

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  17. @jakevdp
    Jake VanderPlas
    color_map = dict(zip(iris.species.unique(),['blue', 'green', 'red']))
    n_panels = len(color_map)
    fig, ax = plt.subplots(1, n_panels, figsize=(n_panels * 5, 3),
    sharex
    =True, sharey=True)
    for i, (species, group) in enumerate(iris.groupby('species')):
    ax[i].scatter(group['petalLength'], group['sepalWidth'],
    color
    =color_map[species],
    alpha
    =0.3, edgecolor=None,
    label
    =species)
    ax[i].legend(frameon=True, title='species')
    plt.xlabel('petalLength')
    plt.ylabel('sepalLength')
    Statistical Visualization: Faceting
    Problem:
    We’re mixing the what with the how

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  18. @jakevdp
    Jake VanderPlas
    Most Useful for Data Science is
    Declarative Visualization
    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.

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  19. @jakevdp
    Jake VanderPlas
    Seaborn: Declarative Visualization
    . . . Almost
    import seaborn as sns
    g = sns.FacetGrid(iris, col="species", hue="species")
    g.map(plt.scatter, "petalLength", "sepalWidth", alpha=0.3)
    g.add_legend();

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  20. @jakevdp
    Jake VanderPlas
    Altair for Declarative Visualization
    from altair import Chart
    Chart(iris).mark_circle(
    opacity=0.3
    ).encode(
    x='petalLength',
    y='sepalWidth',
    color='species'
    )

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  21. @jakevdp
    Jake VanderPlas
    Altair.
    Declarative statistical visualization library for Python,
    driven by Vega-Lite
    http://github.com/altair-viz/altair
    Collaboration with Brian Granger (Jupyter team), myself,
    and UW’s Interactive Data Lab

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  22. @jakevdp
    Jake VanderPlas
    Changing the Encoding is Trivial
    from altair import Chart
    Chart(iris).mark_circle(
    opacity=0.3
    ).encode(
    x='petalLength',
    y='sepalWidth',
    color='species',
    )

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  23. @jakevdp
    Jake VanderPlas
    Changing the Encoding is Trivial
    from altair import Chart
    Chart(iris).mark_circle(
    opacity=0.3
    ).encode(
    x='petalLength',
    y='sepalWidth',
    color='species',
    column='species'
    )

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  24. #JSM2016
    Jake VanderPlas
    So What Is Altair?

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  25. #JSM2016
    Jake VanderPlas
    D3 is Everywhere . . .
    (click for live version)

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  26. #JSM2016
    Jake VanderPlas
    But working in D3 can
    be challenging . . .

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  27. #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.

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  28. #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.

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  29. #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.

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  30. #JSM2016
    Jake VanderPlas
    Bar Chart: Altair
    Altair is a Python API for creating
    Vega-Lite specifications.

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  31. @jakevdp
    Jake VanderPlas
    From Declarative API
    to declarative Grammar
    url = load_dataset('iris', url_only=True)
    chart = Chart(url).mark_circle(
    opacity=0.3
    ).encode(
    x='petalLength:Q',
    y='sepalWidth:Q',
    color='species:N',
    )
    chart.display()

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  32. @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'}

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  33. #JSM2016
    Jake VanderPlas
    Key Features of Altair:
    - Designed with Statistical Visualizations in mind
    - Data specified in Tidy Format & linked to a
    declared type: Quantitative, Nominal, Ordinal,
    Temporal
    - Well-defined set of marks to represent data
    - Encoding Channels map
    data features (i.e. columns) to
    visual encodings (e.g. x, y, color, size, etc.)
    - Simple data transformations supported
    natively

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  34. #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-Pollination: 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

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  35. @jakevdp
    Jake VanderPlas
    Altair/Vega-Lite supports many plot types:

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  36. @jakevdp
    Jake VanderPlas
    Altair/Vega-Lite supports many plot types:

    View full-size slide

  37. @jakevdp
    Jake VanderPlas
    Altair/Vega-Lite supports many plot types:

    View full-size slide

  38. @jakevdp
    Jake VanderPlas
    Altair/Vega-Lite supports many plot types:

    View full-size slide

  39. @jakevdp
    Jake VanderPlas
    Altair/Vega-Lite supports many plot types:

    View full-size slide

  40. @jakevdp
    Jake VanderPlas
    Altair/Vega-Lite supports many plot types:

    View full-size slide

  41. #JSM2016
    Jake VanderPlas
    (Visualizations from
    jakevdp/altair-examples).

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  42. @jakevdp
    Jake VanderPlas
    Some Live Examples . . .
    See the notebook at
    https://github.com/jakevdp/talks/blob/master/2016-11-9-Altair.ipynb

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  43. @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()

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  44. @jakevdp
    Jake VanderPlas
    Altair’s Development is Active!
    - More plot types
    - Higher-level Statistical routines
    - Improve layering API
    - Vega-Tooltip interaction
    - Vega-Lite's Grammar of Interaction
    (See [1])
    [1] http://idl.cs.washington.edu/papers/vega-lite/

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  45. @jakevdp
    Jake VanderPlas
    Email: [email protected]
    Twitter: @jakevdp
    Github: jakevdp
    Web: http://vanderplas.com
    Blog: http://jakevdp.github.io
    Thank You!

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