Python Bokeh tutorial - Interactive Data Visualization with Bokeh

In this article, you will learn how to install Bokeh (and its dependencies) as well as the fundamental building blocks for visualization using Bokeh. Additionally, you'll discover how to design and customize simple plots.

What is Bokeh?

A Python module called Bokeh is employed to create extremely interactive graphs and visualizations. HTML and JavaScript are used to do this in bokeh. As a result, it becomes a potent tool for developing projects, unique charts, and web-based applications.Bokeh supports a number of plot types, including scatter plots, line plots, bar charts, heat maps, and more.

What distinguishes Bokeh from other tools?

There are numerous Python libraries available in the visualizations sector for building dashboards and visualizations, such as:

Bokeh
Streamlit
Plotly
Geoplotlib
Matplotlib
Dash
Seaborn

The following are some uses for Bokeh:

Creating graphics that are vibrant and expressive
Constructing interactive visualizations for contemporary browsers
Making server-backed applications or standalone documents
Constructing dashboards for massive, dynamic, or streaming data

It would be best if you read this Bokeh tutorial whether you are a machine learning engineer or data scientist. You must be familiar with Python's fundamental concepts and be able to install and import packages in order to complete this training. You'll discover how to use the Bokeh library to build interactive visualizations and publish them online. You may develop interactive charts, dashboards, and data apps with the aid of Bokeh.

It will install each dependency. If every dependency has been installed, pip may be used to install Bokeh from PyPI. In the terminal, enter the following command.

Let's make a straightforward plot after installation and learning about the fundamentals of Bokeh.

Line Graph :

Input:

# importing the required modules
from Bokeh.plotting import figure, output_file, show

# instantiating the figure object
graph = figure(title = "Bokeh Line Graph")

# the points to be plotted in the graph
x = [1, 2, 3, 4, 5]
y = [6, 7, 8, 9, 10]

# plotting the line graph as per the points
graph.line(x, y)

# displaying the model
show(graph)

Output:

Python Bokeh tutorial - Interactive Data Visualization with Bokeh

Input:

# importing the required modules
from Bokeh.plotting import figure, output_file, show

# instantiating the figure object
graph = figure(title="Bokeh Line Graph")

# the points to be plotted
x = [1, 2, 3, 4, 5]
y = [6, 5, 4, 3, 1]

# plotting the 1st line graph
graph.line(x, x, legend_label="Line 1")

# plotting the 2nd line graph with a
# different color
graph.line(y, x, legend_label="Line 2",
		line_color="red")

# displaying the model
show(graph)

Output:

Bar Graph :

Syntax:vbar(parameters)

Input:

# importing the required modules
from Bokeh.plotting import figure, show

animals = ['lion', 'tiger', 'elephant', 'rhino', 'buffalo']
weight_tonnes = [190, 90, 3000, 2300, 590]

p = figure(x_range=animals, height=250, title="Five animals weight", x_axis_label = "Animals", y_axis_label = "Weights",
           toolbar_location=None, tools="")

p.vbar(x=animals, top=weight_tonnes, width=0.75)

p.xgrid.grid_line_color = None
p.y_range.start = 0

show(p)

Output:

Patch Plot :

Syntax:patch(parameters)

Input:

# importing the modules
from Bokeh.plotting import figure, output_file, show
from bokeh.palettes import magma
import random

	
# instantiating the figure object
graph = figure(title = "Bokeh Patch Plot")

# points to be plotted
x = [n for n in range(250)]
y = [random.random() + 1 for n in range(250)]

# plotting the graph
graph.patch(x, y)

# displaying the model
show(graph)

Output:

Pie Chart :

Syntax: wedge(parameters)

Input:

from Bokeh.plotting import figure, output_file, show
graph = figure(title = "Bokeh Wedge Graph")
# the points to be plotted
x = 0
y = 0
# radius of the wedge
radius = 30

# start angle of the wedge
start_angle = 4

# end angle of the wedge
end_angle = 5

# plotting the graph
graph.wedge(x, y, radius = radius,
			start_angle = start_angle,
			end_angle = end_angle)
	
# displaying the model
show(graph)

Output:

Scatter Plot :

Syntax:scatter(parameters)

Input:

# importing the required modules
from Bokeh.plotting import figure, output_file, show
from bokeh.palettes import magma
import random

# instantiating the figure object
graph = figure(title = "Bokeh Scatter Graph")

# points to be plotted
x = [n for n in range(200)]
y = [random.random() + 1 for n in range(200)]


# plotting the graph
graph.scatter(x, y)

# displaying the model
show(graph)

Output:

Circle Plot :

Input:

# Import required packages
from bokeh.io import output_file, show
from Bokeh.plotting import figure
# Create x and y data points
x = [1,2,3,4,5]
y = [5,7,2,2,4]
# Create plot
plot = figure(title = "Circle Plot" )
plot.circle(x,y, size = 25, alpha = 0.75)
# Add labels

# Output the plot
show(plot)

Output:

A Python library for building interactive data visualizations is called Bokeh. The fundamentals of making Bokeh plots, personalizing visualizations, and including interactivity have all been addressed in this article. You can explore Bokeh's broad capabilities for creating intricate dashboards and interactive web applications as you grow more familiar with them.

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