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JavaScript Charting for iPython/Jupyter notebooks

Project description

IPlotter

PyPI version PyPI PyPI

JavaScript charting in ipython/jupyter notebooks

iplotter is a simple package for generating interactive charts in ipython/jupyter notebooks using popular JavaScript Libraries from python data structures (dictionaries, lists, etc.)

Installation

To install the most recent stable release run pip install iplotter.

To install the latest version run pip install git+git://github.com/niloch/iplotter.git@master or git clone https://github.com/niloch/iplotter.git followed by pip install -e iplotter/

C3.js

C3 is a charting library based on d3.js for making interactive and easy to understand charts, graphs, and plots. Charts have animated transitions for hiding/displaying data.

plotly.js

Plotly.js is a charting library based on d3.js. While plotly provides a native client in python, it requires the user to create an account and by default makes all plots public. plotly.js can be used without creating an account and are rendered locally to keep data private.

Chart.js

Chart.js provides 6 chart types via HTML5 canvas elements with tooltips/hover events in very a lightweight library.

Chartist.js

Simple and Responsive SVG charts with media queries and animations.

Google Charts

Simple and Powerful interactive charts with SVG/VML formats.

Usage

iplotter attempts to maintain a consistent API across JavaScript Libraries as much as possible, with slight parameter variations. Each library class supports the following functions: render, plot, save, plot_and_save. The python chart data,layout,options must be structured according to the JSON equivalent for each library (see C3.js, plotly.js,Chart.js, Chartist.js and Google Charts for more examples). Plots can be rendered in ipython notebooks and saved to the current directory as html files.

Examples

C3 Stacked Area Spline Chart

from iplotter import C3Plotter

plotter = C3Plotter()

chart = {
    "data": {
        "columns": [
            ['data1', 300, 350, 300, 0, 0, 120],
            ['data2', 130, 100, 140, 200, 150, 50],
            ['data3', 180, 75, 265, 100, 50, 100]
        ],
        "types": {
            "data1": 'area-spline',
            "data2": 'area-spline',
            "data3": 'area-spline'
        },
        "groups": [['data1', 'data2', 'data3']]
    }
}

plotter.plot(chart)

Plot1

plotly.js HeatMap

from iplotter import PlotlyPlotter

plotter = PlotlyPlotter()

data = [
    {
        'colorscale': 'YIGnBu',
        'reversescale': True,
        'type': 'heatmap',
        'x': ['class1', 'class2', 'class3'],
        'y': ['class1', 'class2', 'class3'],
        'z': [[0.7,  0.2,  0.1],
              [0.2,  0.7,  0.1],
              [0.15,  0.27,  0.56]]
    }
]

layout = {
    "title": 'Title',
    "xaxis": {
        "tickangle": -45
    },
}

plotter.plot_and_save(data, layout=layout, w=600, h=600, filename='heatmap1', overwrite=True)

Plot3

Chart.js Radar Chart

from iplotter import ChartJSPlotter

plotter = ChartJSPlotter()

data = {
    "labels": ["Eating", "Drinking", "Sleeping", "Designing", "Coding",
               "Cycling", "Running"],
    "datasets": [
        {
            "label": "Trace 1",
            "backgroundColor": "rgba(179,181,198,0.2)",
            "borderColor": "rgba(179,181,198,1)",
            "pointBackgroundColor": "rgba(179,181,198,1)",
            "pointBorderColor": "#fff",
            "pointHoverBackgroundColor": "#fff",
            "pointHoverBorderColor": "rgba(179,181,198,1)",
            "data": [65, 59, 30, 81, 56, 55, 40]
        }, {
            "label": "Trace 2",
            "backgroundColor": "rgba(255,99,132,0.2)",
            "borderColor": "rgba(255,99,132,1)",
            "pointBackgroundColor": "rgba(255,99,132,1)",
            "pointBorderColor": "#fff",
            "pointHoverBackgroundColor": "#fff",
            "pointHoverBorderColor": "rgba(255,99,132,1)",
            "data": [28, 48, 40, 19, 96, 27, 100]
        }
    ]
}

plotter.plot_and_save(data, 'radar', options=None)

Plot4

Chartist.js Bipolar Area Chart

from iplotter import ChartistPlotter

plotter = ChartistPlotter()

data = {
    "labels": [1, 2, 3, 4, 5, 6, 7, 8],
    "series": [
        [1, 2, 3, 1, -2, 0, 1, 0], [-2, -1, -2, -1, -2.5, -1, -2, -1],
        [0, 0, 0, 1, 2, 2.5, 2, 1], [2.5, 2, 1, 0.5, 1, 0.5, -1, -2.5]
    ]
}
options = {
    "high": 4,
    "low": -3,
    "showArea": True,
    "showLine": False,
    "showPoint": False,
    "height": 420,
    "width": 700
}

plotter.save(data, chart_type="Line", options=options)

Plot6

Google Charts stacked Column Chart

from iplotter import GCPlotter

plotter = GCPlotter()

data = [
    ['Genre', 'Fantasy & Sci Fi', 'Romance', 'Mystery/Crime', 'General',
     'Western', 'Literature', {"role": 'annotation'}],
    ['2010', 10, 24, 20, 32, 18, 5, ''],
    ['2020', 16, 22, 23, 30, 16, 9, ''],
    ['2030', 28, 19, 29, 30, 12, 13, '']
]

options = {
    "width": 600,
    "height": 400,
    "legend": {"position": 'top', "maxLines": 3},
    "bar": {"groupWidth": '75%'},
    "isStacked": "true",
}

plotter.plot(data, chart_type="ColumnChart",chart_package='corechart', options=options)

Plot7

Multple Charts and Mixing Libraries

Saving multiple charts to one file or displaying multiple charts in one iframe can be achieved by concatenating html strings returned by the render function. The plotter's head attribute contains the script tags for loading the necessary JavasScript libraries and div_ids must be unique. Charts from different libraries can be mixed together.

from iplotter import PlotlyPlotter, C3Plotter
from IPython.display import HTML

plotly_plotter = PlotlyPlotter()

c3_plotter = C3Plotter()

plotly_chart = [{
    "type": 'choropleth',
    "locationmode": 'USA-states',
    "locations": ["AL", "AK", "AZ", "AR", "CA", "CO", "CT", "DE", "FL", "GA",
                  "HI", "ID", "IL", "IN", "IA", "KS", "KY", "LA", "ME", "MD",
                  "MA", "MI", "MN", "MS", "MO", "MT", "NE", "NV", "NH", "NJ",
                  "NM", "NY", "NC", "ND", "OH", "OK", "OR", "PA", "RI", "SC",
                  "SD", "TN", "TX", "UT", "VT", "VA", "WA", "WV", "WI", "WY"],
    "z": [1390.63, 13.31, 1463.17, 3586.02, 16472.88, 1851.33, 259.62, 282.19,
          3764.09, 2860.84, 401.84, 2078.89, 8709.48, 5050.23, 11273.76,
          4589.01, 1889.15, 1914.23, 278.37, 692.75, 248.65, 3164.16, 7192.33,
          2170.8, 3933.42, 1718, 7114.13, 139.89, 73.06, 500.4, 751.58, 1488.9,
          3806.05, 3761.96, 3979.79, 1646.41, 1794.57, 1969.87, 31.59, 929.93,
          3770.19, 1535.13, 6648.22, 453.39, 180.14, 1146.48, 3894.81, 138.89,
          3090.23, 349.69],
    "text":
    ["Alabama", "Alaska", "Arizona", "Arkansas", " California", "Colorado",
     "Connecticut", "Delaware", "Florida", "Georgia", "Hawaii", "Idaho",
     "Illinois", "Indiana", "Iowa", "Kansas", "Kentucky", "Louisiana", "Maine",
     "Maryland", "Massachusetts", "Michigan", "Minnesota", "Mississippi",
     "Missouri", "Montana", "Nebraska", "Nevada", "New Hampshire",
     "New Jersey", "New Mexico", "New York", "North Carolina", "North Dakota",
     "Ohio", "Oklahoma", "Oregon", "Pennsylvania", "Rhode Island",
     "South Carolina", "South Dakota", "Tennessee", "Texas", "Utah", "Vermont",
     "Virginia", "Washington", "West Virginia", "Wisconsin", "Wyoming"],
    "zmin": 0,
    "zmax": 17000,
    "colorscale": [
        [0, 'rgb(242,240,247)'], [0.2, 'rgb(218,218,235)'],
        [0.4, 'rgb(188,189,220)'], [0.6, 'rgb(158,154,200)'],
        [0.8, 'rgb(117,107,177)'], [1, 'rgb(84,39,143)']
    ],
    "colorbar": {
        "title": 'Millions USD',
        "thickness": 0.2
    },
    "marker": {
        "line": {
            "color": 'rgb(255,255,255)',
            "width": 2
        }
    }
}]

plotly_layout = {
    "title": '2011 US Agriculture Exports by State',
    "geo": {
        "scope": 'usa',
        "showlakes": True,
        "lakecolor": 'rgb(255,255,255)'
    }
}

c3_chart = {
    "data": {
        "columns": [
            ['data1', 300, 350, 300, 0, 0, 120],
            ['data2', 130, 100, 140, 200, 150, 50],
            ['data3', 180, 75, 265, 100, 50, 100]
        ],
        "type": "pie",
    }
}

# plotter.head will return the html string containing script tags for loading the plotly.js/C3.js libraries
multiple_plot_html = plotly_plotter.head + c3_plotter.head

multiple_plot_html += c3_plotter.render(data=c3_chart, div_id="chart_1")

multiple_plot_html += plotly_plotter.render(
    data=plotly_chart, layout=plotly_layout, div_id="chart_2")

# display multiple plots in iframe
HTML(c3_plotter.iframe.format(source=multiple_plot_html, w=600, h=900))
# Write multiple plots to file
with open("multiple_plots.html", 'w') as outfile:
    outfile.write(multiple_plot_html)

Plot5

Exporting plots to PNG images with Selenium

Saved interactive HTML plots can be converted to static png images programatically for inclusion in other documents via a Selenium helper class. The user will need to download a compatible webdriver and include it in their PATH. The expected default is the Chrome webdriver

Using the context manager syntax is recommended as in with VirtualBrowser() as browser to ensure the browswer session is properly released. However it can be used as a normal object by calling browser().quit() to end the session.

from iplotter import C3Plotter, ChartJSPlotter, VirtualBrowser

plotter1 = C3Plotter()
plotter2 = ChartJSPlotter()

####  specify data for charts here...

plotter1.save(data1, filename="chart1")  # save first plot to chart1.html
plotter2.save(data2, filename="chart2")  # save second plot to chart2.html

charts = ["chart1", "chart2"]

with VirtualBrowser() as browser:
    for chart in charts:
        browser.save_as_png(
            filename=chart, width=300,
            height=200)  # save html chart to filename + '.png'

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