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 exploratory data analysis

Since we have worked extensively with the S&P 500 stock data from Kaggle, we are going to be using that dataset in order to create our application. The dataset can be found here: https://www.kaggle.com/camnugent/sandp500/data.

The first step is to read the data into Jupyter Notebook and understand what the data looks like. This can be done using the code shown here:

#Import packages

import pandas as pd

#Read the data into the notebook

df = pd.read_csv('all_stocks_5yr.csv')

#Extract information about the data

df.info()

This renders the output shown in this screenshot:

This sheds information on the number of rows the dataset has, the data types of each column, the number of variables, and any missing values.

The next step is to understand the kind of information contained in all the columns of your dataset. We can do this by using the...