In [1]:

    

import numpy as np


    

In [2]:

    

from bokeh.plotting import figure, gridplot, show, output_notebook
from bokeh.models import ColumnDataSource


    

In [3]:

    

output_notebook()


    

    





    
        
        
        
    

        
        BokehJS successfully loaded.
    

In [4]:

    

N = 300
x = np.linspace(0, 4*np.pi, N)
y1 = np.sin(x)
y2 = np.cos(x)
y3 = np.sin(x) + np.cos(x)


    

In [5]:

    

s1 = figure(plot_width=350, plot_height=350)
s1.scatter(x, y1)

# Linked panning in Bokeh is expressed by sharing ranges between
# plots. Note below that s2 is reated with the `x_range` and `y_range` 
# keyword arguments, and supplied with the same ranges from s1. Here, 
# this links both axes together.
s2 = figure(plot_width=350, plot_height=350, 
            x_range=s1.x_range, y_range=s1.y_range)
s2.scatter(x, y2)

# It is possible to share just one range or the other, to link plots
# along only one dimension. For the third plot, we only link the x-axis
s3 = figure(plot_width=350, plot_height=350, x_range=s1.x_range)
s3.scatter(x, y3)


    

    
Out[5]:





<bokeh.plotting.Figure at 0x1020e0850>

In [6]:

    

p = gridplot([[s1,s2, s3]])
show(p)


    

In [ ]: