Book Image

Hands-On Data Visualization with Bokeh

By : Kevin Jolly
Book Image

Hands-On Data Visualization with Bokeh

By: Kevin Jolly

Overview of this book

Adding a layer of interactivity to your plots and converting these plots into applications hold immense value in the field of data science. The standard approach to adding interactivity would be to use paid software such as Tableau, but the Bokeh package in Python offers users a way to create both interactive and visually aesthetic plots for free. This book gets you up to speed with Bokeh - a popular Python library for interactive data visualization. The book starts out by helping you understand how Bokeh works internally and how you can set up and install the package in your local machine. You then use a real world data set which uses stock data from Kaggle to create interactive and visually stunning plots. You will also learn how to leverage Bokeh using some advanced concepts such as plotting with spatial and geo data. Finally you will use all the concepts that you have learned in the previous chapters to create your very own Bokeh application from scratch. By the end of the book you will be able to create your very own Bokeh application. You will have gone through a step by step process that starts with understanding what Bokeh actually is and ends with building your very own Bokeh application filled with interactive and visually aesthetic plots.
Table of Contents (10 chapters)

The difference between static and interactive plotting

In the world of data visualization, there are three main libraries using Python that dominate the market, and these are as follows:

  • Matplotlib
  • Seaborn
  • Bokeh

The first two, Matplotlib and Seaborn, let you plot static plots—plots that do not change and plots that cannot be interacted with. These plots are useful and add value when performing exploratory data analysis, as they are quick and easy to implement and very fast to execute.

The third plotting library, Bokeh, lets you plot interactive plots—plots that change when the user interacts with them. These plots are useful when you want to give your audience a wide range of options and tools for inferring and looking at data from various angles.