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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

2. Static Visualization – Global Patterns and Summary Statistics

Learning Objectives

By the end of this chapter, you will be able to:

Explain various visualization techniques for different contexts
Identify global patterns of one or more features in a dataset
Create plots to represent global patterns in data: scatter plots, hexbin plots, contour plots, and heatmaps
Create plots that present summary statistics of data: histograms (revisited), box plots, and violin plots

In this chapter, we'll explore different visualization techniques for presenting global patterns and summary statistics of data.

You have been reading a chapter from

Interactive Data Visualization with Python - Second Edition

Published in: Apr 2020

Publisher:

ISBN-13: 9781800200944

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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.

See other products by Shubhangi Hora

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.

See other products by Sharath Chandra Guntuku

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