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Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Bokeh can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications.

To get started using Bokeh to make your visualizations, begin with the User Guide.

For examples of how you might use Bokeh with your own data, peruse the Gallery.

For detailed information about specific Bokeh components, consult the Reference.

If you are interested in contributing to Bokeh, or extending the library, check out the Developers Guide.

If you’d like to search for a particular topic, use the search box below:

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import numpy as np
import pandas as pd

from bokeh.document import Document
from bokeh.embed import file_html
from bokeh.layouts import column, gridplot
from bokeh.models import (Circle, ColumnDataSource, Div, Grid,
                          Line, LinearAxis, Plot, Range1d,)
from bokeh.resources import INLINE
from bokeh.util.browser import view

raw_columns=[
[10.0,   8.04,   10.0,   9.14,   10.0,   7.46,   8.0,    6.58],
[8.0,    6.95,   8.0,    8.14,   8.0,    6.77,   8.0,    5.76],
[13.0,   7.58,   13.0,   8.74,   13.0,   12.74,  8.0,    7.71],
[9.0,    8.81,   9.0,    8.77,   9.0,    7.11,   8.0,    8.84],
[11.0,   8.33,   11.0,   9.26,   11.0,   7.81,   8.0,    8.47],
[14.0,   9.96,   14.0,   8.10,   14.0,   8.84,   8.0,    7.04],
[6.0,    7.24,   6.0,    6.13,   6.0,    6.08,   8.0,    5.25],
[4.0,    4.26,   4.0,    3.10,   4.0,    5.39,   19.0,   12.5],
[12.0,   10.84,  12.0,   9.13,   12.0,   8.15,   8.0,    5.56],
[7.0,    4.82,   7.0,    7.26,   7.0,    6.42,   8.0,    7.91],
[5.0,    5.68,   5.0,    4.74,   5.0,    5.73,   8.0,    6.89]]

quartet = pd.DataFrame(data=raw_columns, columns=
                       ['Ix','Iy','IIx','IIy','IIIx','IIIy','IVx','IVy'])


circles_source = ColumnDataSource(
    data = dict(
        xi   = quartet['Ix'],
        yi   = quartet['Iy'],
        xii  = quartet['IIx'],
        yii  = quartet['IIy'],
        xiii = quartet['IIIx'],
        yiii = quartet['IIIy'],
        xiv  = quartet['IVx'],
        yiv  = quartet['IVy'],
    )
   )

x = np.linspace(-0.5, 20.5, 10)
y = 3 + 0.5 * x
lines_source = ColumnDataSource(data=dict(x=x, y=y))

xdr = Range1d(start=-0.5, end=20.5)
ydr = Range1d(start=-0.5, end=20.5)

def make_plot(title, xname, yname):
    plot = Plot(x_range=xdr, y_range=ydr, plot_width=400, plot_height=400,
                background_fill_color='#efefef')
    plot.title.text = title

    xaxis = LinearAxis(axis_line_color=None)
    plot.add_layout(xaxis, 'below')

    yaxis = LinearAxis(axis_line_color=None)
    plot.add_layout(yaxis, 'left')

    plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
    plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))

    line = Line(x='x', y='y', line_color="#666699", line_width=2)
    plot.add_glyph(lines_source, line)

    circle = Circle(
        x=xname, y=yname, size=12,
        fill_color="#cc6633", line_color="#cc6633", fill_alpha=0.5
    )
    plot.add_glyph(circles_source, circle)

    return plot

#where will this comment show up
I   = make_plot('I',   'xi',   'yi')
II  = make_plot('II',  'xii',  'yii')
III = make_plot('III', 'xiii', 'yiii')
IV  = make_plot('IV',  'xiv',  'yiv')

grid = gridplot([[I, II], [III, IV]], toolbar_location=None)

div = Div(text="""
<h1>Anscombe's Quartet</h1>
<p>Anscombe's quartet is a collection of four small datasets that have nearly
identical simple descriptive statistics (mean, variance, correlation, and linear
regression lines), yet appear very different when graphed.
</p>
""")

doc = Document()
doc.add_root(column(div, grid, sizing_mode="scale_width"))

if __name__ == "__main__":
    doc.validate()
    filename = "anscombe.html"
    with open(filename, "w") as f:
        f.write(file_html(doc, INLINE, "Anscombe's Quartet"))
    print("Wrote %s" % filename)
    view(filename)
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