Interactive Data Visualization with Python E-Book

Abha Belorkar

• Herausgeber: Packt Publishing
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 249

Veröffentlichungsjahr: 2020

Beschreibung

Create your own clear and impactful interactive data visualizations with the powerful data visualization libraries of Python

Key Features

• Study and use Python interactive libraries, such as Bokeh and Plotly
  
  


• Explore different visualization principles and understand when to use which one
  
  


• Create interactive data visualizations with real-world data

Book Description

With so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Interactive Data Visualization with Python sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python.

You'll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirements. After you get a hang of the various non-interactive visualization libraries, you'll learn the principles of intuitive and persuasive data visualization, and use Bokeh and Plotly to transform your visuals into strong stories. You'll also gain insight into how interactive data and model visualization can optimize the performance of a regression model.

By the end of the course, you'll have a new skill set that'll make you the go-to person for transforming data visualizations into engaging and interesting stories.

What you will learn

• Explore and apply different interactive data visualization techniques
  
  


• Manipulate plotting parameters and styles to create appealing plots
  
  


• Customize data visualization for different audiences
  
  


• Design data visualizations using interactive libraries
  
  


• Use Matplotlib, Seaborn, Altair and Bokeh for drawing appealing plots
  
  


• Customize data visualization for different scenarios

Who this book is for

This book intends to provide a solid training ground for Python developers, data analysts and data scientists to enable them to present critical data insights in a way that best captures the user's attention and imagination. It serves as a simple step-by-step guide that demonstrates the different types and components of visualization, the principles, and techniques of effective interactivity, as well as common pitfalls to avoid when creating interactive data visualizations. Students should have an intermediate level of competency in writing Python code, as well as some familiarity with using libraries such as pandas.

Leseprobe

Interactive Data Visualization with Python

Second Edition

Present your data as an effective and compelling story

Abha Belorkar

Sharath Chandra Guntuku

Shubhangi Hora

Anshu Kumar

Interactive Data Visualization with Python

Second Edition

Copyright © 2020 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Authors: Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, and Anshu Kumar

Technical Reviewer: Saurabh Dorle

Managing Editor: Ranu Kundu

Acquisitions Editor: Kunal Sawant

Production Editor: Shantanu Zagade

Editorial Board: Shubhopriya Banerjee, Bharat Botle, Ewan Buckingham, Mahesh Dhyani, Manasa Kumar, Alex Mazonowicz, Bridget Neale, Dominic Pereira, Shiny Poojary, Abhisekh Rane Erol Staveley, Ankita Thakur, Nitesh Thakur, and Jonathan Wray.

First published: October 2019

Second edition: April 2020

Production Reference: 1130420

ISBN: 978-1-80020-094-4

Published by Packt Publishing Ltd.

Livery Place, 35 Livery Street

Birmingham B3 2PB, UK

Table of Contents

Preface   i

1. Introduction to Visualization with Python – Basic and Customized Plotting   1

Introduction   2

Handling Data with pandas DataFrame   3

Reading Data from Files   3

Exercise 1: Reading Data from Files   3

Observing and Describing Data   4

Exercise 2: Observing and Describing Data   4

Selecting Columns from a DataFrame   8

Adding New Columns to a DataFrame   8

Exercise 3: Adding New Columns to the DataFrame   9

Applying Functions on DataFrame Columns   10

Exercise 4: Applying Functions on DataFrame columns   11

Exercise 5: Applying Functions on Multiple Columns   13

Deleting Columns from a DataFrame   14

Exercise 6: Deleting Columns from a DataFrame   14

Writing a DataFrame to a File   16

Exercise 7: Writing a DataFrame to a File   16

Plotting with pandas and seaborn   18

Creating Simple Plots to Visualize a Distribution of Variables   18

Exercise 8: Plotting and Analyzing a Histogram    19

Bar Plots   25

Exercise 9: Creating a Bar Plot and Calculating the Mean Price Distribution    25

Exercise 10: Creating Bar Plots Grouped by a Specific Feature   30

Tweaking Plot Parameters   31

Exercise 11: Tweaking the Plot Parameters of a Grouped Bar Plot   32

Annotations   35

Exercise 12: Annotating a Bar Plot   36

Activity 1: Analyzing Different Scenarios and Generating the Appropriate Visualization   39

Summary   45

2. Static Visualization – Global Patterns and Summary Statistics   47

Introduction   48

Creating Plots that Present Global Patterns in Data   48

Scatter Plots   49

Exercise 13: Creating a Static Scatter Plot   50

Hexagonal Binning Plots   51

Exercise 14: Creating a Static Hexagonal Binning Plot   51

Contour Plots   53

Exercise 15: Creating a Static Contour Plot   53

Line Plots   54

Exercise 16: Creating a Static Line Plot   55

Exercise 17: Presenting Data across Time with multiple Line Plots   58

Heatmaps   60

Exercise 18: Creating and Exploring a Static Heatmap   60

The Concept of Linkage in Heatmaps   66

Exercise 19: Creating Linkage in Static Heatmaps   66

Creating Plots That Present Summary Statistics of Your Data   71

Histogram Revisited   71

Example 1: Histogram Revisited   72

Box Plots   73

Exercise 20: Creating and Exploring a Static Box Plot   73

Violin Plots   76

Exercise 21: Creating a Static Violin Plot   77

Activity 2: Design Static Visualization to Present Global Patterns and Summary Statistics   78

Summary   83

3. From Static to Interactive Visualization   85

Introduction    86

Static versus Interactive Visualization   88

Applications of Interactive Data Visualizations    93

Getting Started with Interactive Data Visualizations   95

Interactive Data Visualization with Bokeh   98

Exercise 22: Preparing Our Dataset   99

Exercise 23: Creating the Base Static Plot for an Interactive Data Visualization   104

Exercise 24: Adding a Slider to the Static Plot   107

Exercise 25: Adding a Hover Tool   108

Interactive Data Visualization with Plotly Express   113

Exercise 26: Creating an Interactive Scatter Plot   113

Activity 3: Creating Different Interactive Visualizations Using Plotly Express   117

Summary   119

4. Interactive Visualization of Data across Strata   121

Introduction   122

Interactive Scatter Plots   122

Exercise 27: Adding Zoom-In and Zoom-Out to a Static Scatter Plot   124

Exercise 28: Adding Hover and Tooltip Functionality to a Scatter Plot   127

Exercise 29: Exploring Select and Highlight Functionality on a Scatter Plot   130

Exercise 30: Generating a Plot with Selection, Zoom, and Hover/Tooltip Functions   133

Selection across Multiple Plots   136

Exercise 31: Selection across Multiple Plots   137

Selection Based on the Values of a Feature   140

Exercise 32: Selection Based on the Values of a Feature   141

Other Interactive Plots in altair   143

Exercise 33: Adding a Zoom-In and Zoom-Out Feature and Calculating the Mean on a Static Bar Plot    144

Exercise 34: An Alternative Shortcut for Representing the Mean on a Bar Plot   150

Exercise 35: Adding a Zoom Feature on a Static Heatmap   153

Exercise 36: Creating a Bar Plot and a Heatmap Next to Each Other   157

Exercise 37: Dynamically Linking a Bar Plot and a Heatmap   160

Activity 4: Generate a Bar Plot and a Heatmap to Represent Content Rating Types in the Google Play Store Apps Dataset   163

Summary   166

5. Interactive Visualization of Data across Time   169

Introduction   170

Temporal Data   170

Types of Temporal Data   171

Why Study Temporal Visualization?   172

Understanding the Relation between Temporal Data and Time-Series Data   174

Examples of Domains That Use Temporal Data   175

Visualization of Temporal Data   176

How Time-Series Data Is Manipulated and Visualized   179

Date/Time Manipulation in pandas   181

Building a DateTime Index   182

Choosing the Right Aggregation Level for Temporal Data   183

Exercise 38: Creating a Static Bar Plot and Calculating the Mean and Standard Deviation in Temporal Data   185

Exercise 39: Calculating zscore to Find Outliers in Temporal Data   190

Resampling in Temporal Data   194

Common Pitfalls of Upsampling and Downsampling   194

Exercise 40: Upsampling and Downsampling in Temporal Data   194

Using shift and tshift to Introduce a Lag in Time-Series Data   199

Exercise 41: Using shift and tshift to Shift Time in Data   199

Autocorrelation in Time Series   201

Interactive Temporal Visualization   203

Bokeh Basics   204