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)

Using Layouts for Effective Presentation

While plotting multiple plots, it is always best to make use of layouts in order to display the plots side by side, or vertically on top of each other, in order to make statistical comparisons between the two plots and make them visually appealing at the same time.

The use of layouts, tabs, and grids effectively while creating plots with Bokeh will also allow you to link multiple plots together by making use of the same axes. This makes the comparison of multiple plots much more accurate than if you were to create the plots in separate cells in your Jupyter Notebook.

In this chapter, you will learn how to:

  • Create multiple plots along the same row
  • Create multiple plots along the same column
  • Create multiple plots in a row and column
  • Create multiple plots using a tabbed layout
  • Create a robust grid layout
  • Link multiple plots together
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