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Interactive Data Visualization with Python

You're reading from   Interactive Data Visualization with Python Present your data as an effective and compelling story

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Product type Paperback
Published in Apr 2020
Publisher
ISBN-13 9781800200944
Length 362 pages
Edition 2nd Edition
Languages
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Authors (4):
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Shubhangi Hora Shubhangi Hora
Author Profile Icon Shubhangi Hora
Shubhangi Hora
Abha Belorkar Abha Belorkar
Author Profile Icon Abha Belorkar
Abha Belorkar
Anshu Kumar Anshu Kumar
Author Profile Icon Anshu Kumar
Anshu Kumar
Sharath Chandra Guntuku Sharath Chandra Guntuku
Author Profile Icon Sharath Chandra Guntuku
Sharath Chandra Guntuku
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Toc

Table of Contents (9) Chapters Close

Preface 1. Introduction to Visualization with Python – Basic and Customized Plotting 2. Static Visualization – Global Patterns and Summary Statistics FREE CHAPTER 3. From Static to Interactive Visualization 4. Interactive Visualization of Data across Strata 5. Interactive Visualization of Data across Time 6. Interactive Visualization of Geographical Data 7. Avoiding Common Pitfalls to Create Interactive Visualizations Appendix

Introduction

In the previous chapters, we went through a variety of techniques for visualizing data effectively based on the type of features in the dataset and learned how to introduce interactivity in plots using the plotly library. The second section of this book, starting with this chapter, will guide you on building interactive visualizations with Python for a variety of contexts. An observation made in the previous chapter was that when it comes to introducing interactivity in certain types of Python plots, plotly can sometimes be verbose, and may involve a steep learning curve. Therefore, in this chapter, we'll introduce altair, a library designed especially for generating interactive plots. We will demonstrate how to create interactive visualizations with altair for data stratified with respect to any categorical variable. For illustration, we will use a publicly available dataset to generate scatter plots and bar plots with the features in the dataset and add a variety...

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