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)

Creating multiple plots along the same row

In order to create multiple plots along the same row, let's first create three unique plots. We will be working with the S&P 500 stock data found on Kaggle (https://www.kaggle.com/camnugent/sandp500/data).

The first step is to read the data and filter it so that we only use the data related to Apple as shown here:

#Import the required packages

import pandas as pd

#Read in the data

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

#Convert the date column into datetime data type

df['date'] = pd.to_datetime(df['date'])

#Filter the data for Apple stocks only

df_apple = df[df['Name'] == 'AAL']

Next, let's construct three unique plots using the code as shown here:

#Import the required packages

from bokeh.io import output_file, show
from bokeh.plotting import figure
from bokeh.plotting import ColumnDataSource...