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)

Introduction to deploying the Bokeh application

In the previous sections, we deployed our Bokeh applications using our local machine, which could then be shared with our colleagues in our internal team.

In order to deploy a Bokeh application, we first wrote a script in Python that included the the plot, the callback function, and the layout. We then gave the script an appropriate name. In the previous example, we gave our scripts the name bokeh.py.

Using the Terminal for Mac/Linux or the shell for Windows, we deployed the application from the directory in which the Python script was located with the command shown here:

bokeh serve --show bokeh.py

This launches the application in the default browser of your choice as:

http://localhost:5006/bokeh

In this case, we were making use of the Bokeh Server in order to run and deploy our application.

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