Python Tutorial

Bokeh is a Python library which is used for data visualization. It creates its plots using HTML and JavaScript languages, and it also targets modern website browsers to provide presentation elegant, concise construction of novel graphics good high-performance interactivity.

In this tutorial, we will learn how to plot multiple plots by using the Bokeh library in Python. We will be using the row() function of the bokeh.layouts module can be used in the show() function of bokeh.io library to illustrate multiple plots by using the bokeh library.

Syntax:

The syntax for show() function is:

In which fig1, fig2, fig3, and so on are objects of the class figure in bokeh.plotting module.

Approach:

For plotting multiple plots on a graph by using the bokeh module, we will use the following approach:

Step 1: We will import all the required libraries and modules
Step 2: We will assign coordinates and then start depicting the plots using the figure class.
Step 3: we will then use the figure object as an argument in the row() function.
Step 4: we will at last use the show function for depicting the visualized return output from the row() function.

Example 1: To Plot Different Plots in the Same Page

# First, we will import all the required libraries and modules
from bokeh.io import output_file as OF
from bokeh.io import show
from bokeh.layouts import row
from bokeh.plotting import figure as figs
 
# Now we will create a new plot
fig1 = figs(plot_width = 400,
              plot_height = 400, title = "Plot 1")
fig1.line([2, 1, 5, 3, 4, 7, 6],
          [1, 4, 3, 5, 2, 7, 7],
          line_width = 4)
 
# Then, we will create another plot
x = y = list(range(10))
fig2 = figs(plot_width = 400,
              plot_height = 400, title = "Plot 2")
fig2.circle(x, y, size = 5)
 
# depict visualization
show(row(fig1, fig2))

Output:

How to Plot Multiple Plots using Bokeh in Python

Example 2: To Plot Different Plots in Same Frame

# First, we will import all the required libraries and modules
from bokeh.io import output_file as OF
from bokeh.io import show
from bokeh.layouts import row
from bokeh.plotting import figure as figs
import numpy as nmp
import random as rdm
 
 
# Now, we will create a new plot
# instantiate the figure object
fig1 = figs(title = "Plot 1")
 
# coordinates
x1 = [[[[1, 2, 1, 0]]],
     [[[2, 4, 1, 2], [2.7, 2.4, 3.4, 3.1]]],
     [[[3, 1, 2]]]]
y1 = [[[[3.5, 2.5, 2.5, 3.5]]],
     [[[1, 1, 2, 3], [1.75, 2.25, 1.25, 2.75]]],
     [[[2, 1, 3]]]]
 
# Now we will give the color values of the poloygons
color1 = ["GREEN", "ORANGE", "BLUE"]
 
# filling the alpha values of the polygons
fill_alpha1 = 1.5
 
# Now, we will plot the graph
fig1.multi_polygons(x = x1, y = y1,
                    color = color1,
                    fill_alpha = fill_alpha1)
 
 
# Then, we will create another plot
# coordinates
x2 = nmp.arange(4)
y2 = x **2
z2 = x *3
p = nmp.linspace(2, 22, 5)
q = nmp.linspace(2, 12, 5)
r = nmp.linspace(2, 32, 7)
a = nmp.arange(34)
 
# Here, we will create an empty figure with specific plot width and height
fig2 = figs(title = "Plot 2")
 
# Then, we will plot the points in the form of circular glyphs
fig2.circle(x = x2, y = y2, color = "YELLOW", size = 20)
 
# Now, we will plot the points in the form of square glyphs
fig2.square(x = x2, z = z2, color = "GREEN", size = 12, alpha = 1.5)
 
# plot the points in the form of hex glyphs
fig2.hex(y = y2, z = z2, color = "Pink", size = 12, alpha = 0.7)
 
# now, we will draw a line between the plotted points
fig2.line(x = x2, y = y2, color = "grey", line_width = 4)
 
# plot the points in the form of inverted triangle glyph
fig2.inverted_triangle(p = p, q = q, color = "Green", size = 22, alpha = 0.4)
 
# plot the points in the form of diamond glyphs
fig2.diamond(x = x2, r = r, color = "RED", size = 16, alpha = 0.8)
 
# plot the points in the form of cross glyphs
fig2.cross(a = a, a = a, size = 14)
 
 
# creating a third plot
# generate the points to be plotted
x = []
y = []
for i in range(100):
    x.append(i)
for i in range(100):
    y.append(1 + rdm.random())
 
# parameters of line 1
line_color = "red"
line_dash = "solid"
legend_label = "Line 1"
 
fig3 = figs(title = "Plot 3")
 
# plotting the line
fig3.line(x, y,
          line_color = line_color,
          line_dash = line_dash,
          legend_label = legend_label)
 
# plot line 2
# generate the points to be plotted
x = []
y = []
for i in range(100):
    x.append(i)
for i in range(100):
    y.append(rdm.random())
 
# parameters of line 2
line_color = "green"
line_dash = "dotdash"
line_dash_offset = 1
legend_label = "Line 2"
 
# plotting the line
fig3.line(x, y,
          line_color = line_color,
          line_dash = line_dash,
          line_dash_offset = line_dash_offset,
          legend_label = legend_label)
 
# depict visualization
show(row(fig1, fig2, fig3))

Output:

Example 3: To Plot Multiple Plots in a Row

# First, we will import all the required libraries and modules
from bokeh.io import output_file as OF
from bokeh.io import show
from bokeh.layouts import row
from bokeh.plotting import figure as figs
 
# now, we will assign coordinates
x = y = list(range(10))
xs = [[[[1, 1, 2, 2]]]]
ys = [[[[4, 3, 3, 4]]]]
 
# creating a new plot
fig1 = figs(title = "Plot 1", plot_width = 250,
              plot_height = 250)
fig1.line(x, y, line_width = 25, color = "Orange")
 
# creating second plot
fig2 = figs(title = "Plot 2", plot_width = 250,
              plot_height = 250)
fig2.circle(x, y, size = 25, color = "Blue")
 
# creating third plot
fig3 = figs(title = "Plot 3", plot_width = 250,
              plot_height = 250)
fig3.square(x, y, size = 25, color = "Green")
 
# create forth plot
fig4 = figs(title = "Plot 4", plot_width = 250,
              plot_height = 250)
fig4.triangle(x, y, size = 25, color = "RED")
 
# creating fifth plot
fig5 = figs(title = "Plot 5", plot_width = 250,
              plot_height = 250)
fig5.multi_polygons(xs, ys, color = "Blue")
 
# creating sixth plot
fig6 = figs(title = "Plot 6", plot_width = 250,
              plot_height = 250)
fig6.line(x, y, line_dash = "dotted", color = "Green")
 
# depict visualization
show(row(fig1, fig2,
         fig3, fig4,
         fig5, fig6))

Output: