Explanation of Data Visualization using Bokeh in Python

7 min readJan 17, 2023

what is Bokeh?

Bokeh is a Python library for creating interactive visualizations for modern web browsers. It can create plots, charts, and other types of visualizations, and it focuses on providing elegant and concise construction of versatile graphics. Bokeh can also be used to create visualizations for use in web applications and dashboards.

Data Visualization?

Data visualization is the process of creating graphical representations of data in order to communicate information effectively and efficiently. The goal of data visualization is to make it easier for people to understand, analyze, and make decisions based on data.

Data visualization can take many forms, including charts, graphs, maps, and diagrams. Common types of charts include bar charts, line charts, scatter plots, and pie charts. Each type of chart is best suited for displaying a specific type of data. For example, bar charts are good for showing the distribution of categorical data, while line charts are good for showing trends in continuous data over time.

Bokeh provides visualization for user in python-

Bokeh is a Python library that provides a high-level interface for creating interactive visualizations for modern web browsers. It can be used to create a wide variety of visualizations, including plots, charts, and other types of graphics. The library is built on top of JavaScript library called BokehJS, which makes it easy for Python developers to create visually appealing and interactive visualizations without needing to have expertise in JavaScript. Bokeh provides a wide range of tools for creating visualizations, including high-level charting interfaces, low-level plotting interfaces, and tools for creating interactive elements like hover tools and widgets. It is a powerful library and it can be use in many area such as data visualization, dashboarding, web application etc.

bokeh.models : A low level interface that provides high flexibility to application developers.
bokeh.plotting : A high level interface for creating visual glyphs.

To run the Bokeh package, we have to run following command in the system-

pip install bokeh

Code to run the Bokeh in Python-

Here is some example code to create a simple Bokeh plot in Python:

from bokeh.plotting import figure, show

# create a new plot with default tools
p = figure(plot_width=400, plot_height=400)# add a circle renderer with x and y coordinates, size, color, and alpha
p.circle([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], size=20, color="navy", alpha=0.5)# show the plot
show(p)

This code imports the necessary functions from the Bokeh library, creates a new plot, adds a circle renderer to the plot with x and y coordinates, size, color, and alpha, and then shows the plot. When the show() function is called, the plot will be displayed in a web browser window.

You can also use Bokeh to create other types of visualizations such as line plots, bar plots, scatter plots, etc. Bokeh also supports more complex visualizations such as hover tools, widgets, and linked panning and brushing. You can find more examples, tutorials and documentations in Bokeh website.

Bokeh is absolutely easy for beginner-

Bokeh is considered relatively easy to learn and use for beginners, as it has a simple and intuitive API. The library is well-documented and there are many tutorials and examples available to help users get started.

One of the key features of Bokeh is its ability to create beautiful and interactive visualizations with very little code. For example, creating a basic line chart with Bokeh takes only a few lines of code, and the library provides a wide range of tools for customizing and styling plots.

However, as with any new technology, it may take some time to become proficient with Bokeh and to be able to create more advanced visualizations. But with some practice and experience, it can be a very powerful tool for data visualization.

In addition, Bokeh also provide a way to create interactive widgets like sliders, dropdown and buttons to make the visualization more interactive and easy to explore, which is a plus point for beginners.

Overall, Bokeh is a great choice for beginners who want to learn data visualization and create interactive visualizations with minimal coding.

Step for data visualization using Bokeh in Python-

Here are the general steps for data visualization using Bokeh in Python:

Import the necessary modules from the Bokeh library, such as figure, show, and any other tools you want to use.
Prepare your data: this could mean loading data from a file or database, cleaning and processing the data, and formatting it for use with Bokeh.
Create a new plot using the figure() function, and set the plot properties such as the size, title, and axis labels.
Add renderers to the plot. The most common renderers are circle, line, rect, triangle, quad, vbar, hbar, text, image, etc. You can also use the scatter(), line(), vbar(), hbar(), rect(), text(), image() etc. functions to add renderers to the plot.
Customize the appearance of the renderers, such as color, line width, and marker size.
Add any additional tools you want to use, such as hover tools, panning and zooming tools, or widgets.
Display the plot using the show() function. This will open the plot in a web browser window.
You can save the plot as a standalone HTML file using the save() function, and then share it with others or embed it in a web page or application.

Note that these are general steps and you may have to adjust them depending on your specific use case and the type of visualization you want to create. Also, Bokeh provides a lot of customization options, and you can use them to create interactive, visually appealing, and informative visualizations.

Important of Bokeh using Python-

Bokeh is a Python library for creating interactive visualizations for modern web browsers.
It allows developers to create beautiful and responsive visualizations for data exploration and presentation.
Bokeh can help to make data more accessible and engaging to a wider audience, as well as providing useful tools for data analysis and exploration.
Additionally, Bokeh can also be easily integrated into web applications and dashboards, making it a powerful tool for data visualization in a variety of contexts.

What type of project we make from Bokeh using Python-

Bokeh can be used to create a wide range of interactive visualizations, including:

Line, scatter, and bar plots for data exploration and analysis.
Interactive maps and geographic visualizations.
Dashboards and data visualizations for web applications.
Time series and financial charts.
Network and graph visualizations.
3D visualizations and animations.
Interactive plots for scientific and engineering simulations.

Bokeh can be used in many different types of projects and industries, such as data analysis in finance, scientific research, digital marketing, and many others. It allows to create rich interactive visualizations that can be easily embedded in web pages, dashboards, and apps. Bokeh also provide a way to create interactive widgets like sliders, dropdown and buttons to make the visualization more interactive and easy to explore.

The features of bokeh in python-

Bokeh is a powerful Python library for creating interactive visualizations, it has many features that make it an attractive choice for data visualization, some of the key features include:

Interactive: Bokeh allows for panning, zooming, and hover tooltips, making it easy to explore and interact with data.
Beautiful and customizable: Bokeh has a wide range of built-in styles and themes that make it easy to create beautiful visualizations. Additionally, it provides a variety of tools for customizing and styling plots.
Responsive and browser-agnostic: Bokeh visualizations are designed to work seamlessly across different web browsers and devices.
Large dataset handling: Bokeh is capable of handling very large datasets, it uses a streaming and progressive approach, which means that it can handle large data without loading everything into memory at once.
Integration with other libraries: Bokeh can be easily integrated with other Python libraries, such as pandas and scikit-learn, for data analysis and manipulation.
Server-side rendering: Bokeh can also be used to create visualizations that run on a server, which can be accessed remotely via a web browser.
Interactivity: Bokeh provides a way to create interactive widgets like sliders, dropdown and buttons to make the visualization more interactive and easy to explore.
Data streaming: Bokeh can stream data in real-time, which allows for dynamic visualizations that update in response to new data.

Links for Data visualization using Bokeh in Python-

Here are some resources that may be helpful for learning data visualization with Bokeh in Python:

The official Bokeh documentation: https://docs.bokeh.org/en/latest/index.html
A gallery of examples and tutorials for using Bokeh: https://docs.bokeh.org/en/latest/docs/gallery.html
A comprehensive tutorial for creating interactive visualizations with Bokeh: https://towardsdatascience.com/a-complete-guide-to-bokeh-for-data-visualization-in-python-d5b1c5a2b8a8
A tutorial on creating interactive dashboards with Bokeh and Flask: https://towardsdatascience.com/how-to-build-a-complex-reporting-dashboard-using-flask-and-bokeh-ece098e71627
A series of tutorials on using Bokeh for data visualization: https://realpython.com/bokeh-python-visualization/
A tutorial on creating interactive maps with Bokeh and GeoPandas: https://towardsdatascience.com/how-to-create-interactive-crime-maps-with-python-folic-ade2152fade5
A tutorial on creating interactive financial charts with Bokeh: https://towardsdatascience.com/how-to-create-interactive-financial-charts-with-python-and-bokeh-36f8a7af3d50

These resources should provide a good starting point for learning data visualization with Bokeh in Python.

Conclusion-

Bokeh is a powerful and easy-to-use Python library for creating interactive visualizations. It provides a wide range of tools and functionality for creating beautiful and engaging visualizations for data exploration and presentation. Bokeh can be used for a variety of projects and industries, such as data analysis in finance, scientific research, digital marketing, and many others.

Bokeh is a great choice for beginners who want to learn data visualization and create interactive visualizations with minimal coding, but also for more advanced users who want to create complex and dynamic visualizations.