A reproduction of Anscombe’s Quartet using the low-level bokeh.models API that also includes HTML content in a Div.




Bokeh APIs:

bokeh.layouts.column, bokeh.layouts.gridplot, bokeh.models.Plot, bokeh.models.LinearAxis

More info:

Grid layout for plots


column, gridplot

import numpy as np

from import show
from bokeh.layouts import column, gridplot
from bokeh.models import (ColumnDataSource, Div, Grid, Line,
                          LinearAxis, Plot, Range1d, Scatter)
from bokeh.sampledata.anscombe import data as df

circle_source = ColumnDataSource(data=df)

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

rng = Range1d(start=-0.5, end=20.5)

def make_plot(title, xname, yname):
    plot = Plot(x_range=rng, y_range=rng, width=400, height=400,
    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(line_source, line)

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

    return plot

#where will this comment show up
I   = make_plot('I',   'Ix',   'Iy')
II  = make_plot('II',  'IIx',  'IIy')
III = make_plot('III', 'IIIx', 'IIIy')
IV  = make_plot('IV',  'IVx',  'IVy')

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.

show(column(div, grid, sizing_mode="scale_width"))