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)

Summary

This chapter has given you an introduction to what glyphs are and how you can use them to create fundamental plots using Bokeh. We also looked at how to customize these plots further.

Glyphs are the fundamental building blocks of Bokeh and are required in order to create more complex, and statistically significant, plots in the future.

In this chapter, you learned how to create four different plots using glyphs. Line plots are commonly used in time series analytics, bar plots are commonly used to compare counts between different categories, patch plots are commonly used to highlight an area of points, and scatter plots, are commonly used to map a relationship between two or more variables.

In the upcoming chapter, we will take these concepts and use them to plot diagrams using NumPy arrays and Pandas DataFrames.