Bokeh's equivalent to matplotlib subplots

Question

I am looking for a way to create a plot the containing several subplots like

fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True)

would do in matplotlib, which then can be addressed by ax0 and ax1. Is there a way to do something similar in Bokeh? In the bokeh examples gallery I only found single plots.

What about the [Iris Splom](http://bokeh.pydata.org/docs/gallery/iris_splom.html) example in the gallery? — wflynny, Oct 27 '14 at 21:26
Thanks @wflynny that looks promising. In the preview it just looked like a single plot. — greole, Oct 27 '14 at 21:33
The current `GridPlot` creates independent plots in an HTML table, so if you preview/save on it, you will get a preview for each individual subplot. There are plans to also provide a grid plot that is layed out on a single canvas, so that a preview would contain all subplots. Bokeh 0.8 would be an estimate for this feature. — bigreddot, Dec 01 '14 at 17:44

score 11 · Answer 1 · answered Jan 21 '15 at 17:20

I think the simpler example you can find is:

import numpy as np
import bokeh.plotting as bk_plotting
import bokeh.models as bk_models

# for the ipython notebook
bk_plotting.output_notebook()

# a random dataset
data = bk_models.ColumnDataSource(data=dict(x=np.arange(10),
                                            y1=np.random.randn(10),
                                            y2=np.random.randn(10)))

# defining the range (I tried with start and end instead of sources and couldn't make it work)
x_range = bk_models.DataRange1d(sources=[data.columns('x')])
y_range = bk_models.DataRange1d(sources=[data.columns('y1', 'y2')])

# create the first plot, and add a the line plot of the column y1
p1 = bk_models.Plot(x_range=x_range,
                    y_range=y_range,
                    title="",
                    min_border=2,
                    plot_width=250,
                    plot_height=250)
p1.add_glyph(data,
             bk_models.glyphs.Line(x='x',
                                   y='y1',
                                   line_color='black',
                                   line_width=2))

# add the axes
xaxis = bk_models.LinearAxis()
p1.add_layout(xaxis, 'below')
yaxis = bk_models.LinearAxis()
p1.add_layout(yaxis, 'left')

# add the grid
p1.add_layout(bk_models.Grid(dimension=1, ticker=xaxis.ticker))
p1.add_layout(bk_models.Grid(dimension=0, ticker=yaxis.ticker))

# add the tools
p1.add_tools(bk_models.PreviewSaveTool())

# create the second plot, and add a the line plot of the column y2
p2 = bk_models.Plot(x_range=x_range,
                    y_range=y_range,
                    title="",
                    min_border=2,
                    plot_width=250,
                    plot_height=250)
p2.add_glyph(data,
             bk_models.glyphs.Line(x='x',
                                   y='y2',
                                   line_color='black',
                                   line_width=2))



# add the x axis
xaxis = bk_models.LinearAxis()
p2.add_layout(xaxis, 'below')

# add the grid
p2.add_layout(bk_models.Grid(dimension=1, ticker=xaxis.ticker))
p2.add_layout(bk_models.Grid(dimension=0, ticker=yaxis.ticker))

# add the tools again (it's only displayed if added to each chart)
p2.add_tools(bk_models.PreviewSaveTool())

# display both
gp = bk_plotting.GridPlot(children=[[p1, p2]])
bk_plotting.show(gp)

Which generates the output:

enter image description here

Bokeh's equivalent to matplotlib subplots

1 Answers1

Linked