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)

Asking the right question

Asking the right question is by far the most important step when it comes to data visualization. What is the answer that you are seeking?

Some of the most common questions that you need to ask yourself before deciding to visualize data are:

  • Do I want to observe how well two features are correlated?
  • Do I suspect potential outliers in my data that I cannot see unless I visualize my data?
  • Do I want to see whether my data shows a particular trend over a period of time?
  • Do I want to observe the distribution of individual features/columns in my data?
  • Do I want to see whether there are clusters/groups within my data that I can potentially extract value from?
  • Do I believe that a visualization can tell my audience a story about the data?

If the answer to any one of these questions is a yes, then you know that you need to visualize your data. The second question...