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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

Product type Paperback

Published in Apr 2020

Publisher

ISBN-13 9781800200944

Length 362 pages

Edition 2nd Edition

Languages

Python

Tools

Matplotlib

Concepts

Data Visualization

Authors (4):

Shubhangi Hora

Abha Belorkar

Anshu Kumar

Sharath Chandra Guntuku

View More author details

Table of Contents (9) Chapters

Preface

About the Book

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

Summary

In this chapter, we learned how interactive data visualizations are a step ahead of static data visualizations due to their ability to respond to human inputs in real time. The range of applications of interactive data visualizations is vast, and we can visualize almost any type of data interactively.

The human inputs that can be incorporated in interactive data visualizations include, but are not limited to, sliders, zoom features, hover tools, and clickable parameters. Bokeh and Plotly Express are two of the most popular and easy Python libraries that create interactive data visualizations. In the next chapter, we will look at how to create beautiful context-based interactive data visualizations.

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Authors (4)

Abha Belorkar

Abha Belorkar

Abha Belorkar is an educator and researcher in computer science. She received her bachelor's degree in computer science from Birla Institute of Technology and Science Pilani, India and her Ph.D. from the National University of Singapore. Her current research work involves the development of methods powered by statistics, machine learning, and data visualization techniques to derive insights from heterogeneous genomics data on neurodegenerative diseases.

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Anshu Kumar

Anshu Kumar

Anshu Kumar is a data scientist with over 5 years of experience in solving complex problems in natural language processing and recommendation systems. He has an M.Tech. from IIT Madras in computer science. He is also a mentor at SpringBoard. His current interests are building semantic search, text summarization, and content recommendations for large-scale multilingual datasets.

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Shubhangi Hora

Shubhangi Hora

Shubhangi Hora is a data scientist, Python developer, and published writer. With a background in computer science and psychology, she is particularly passionate about healthcare-related AI, including mental health. Shubhangi is also a trained musician.

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Sharath Chandra Guntuku

Sharath Chandra Guntuku

Sharath Chandra Guntuku is a researcher in natural language processing and multimedia computing. He received his bachelor's degree in computer science from Birla Institute of Technology and Science, Pilani, India and his Ph.D. from Nanyang Technological University, Singapore. His research aims to leverage large-scale social media image and text data to model social health outcomes and psychological traits. He uses machine learning, statistical analysis, natural language processing, and computer vision to answer questions pertaining to health and psychology in individuals and communities.

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