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 an insightful visualization

Now that we have a fundamental idea of what our data contains, we can proceed to making the visualization. The first step is to ensure we have the foundation of the visualization ready.

Creating the base plot

The foundation consists of the base plot that you want to visualize. In our case, we want to see how the volume of stocks traded over a period of time correlates with the high prices. In order to build this application, we use the code shown here:

#Import the required packages

from bokeh.io import curdoc
from bokeh.models import ColumnDataSource
from bokeh.plotting import figure
import pandas as pd

#Read the data into the notebook

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

#Convert...