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Interactive Data Visualization with Python - Second Edition

You're reading from Interactive Data Visualization with Python - Second Edition

Product type Book

Published in Apr 2020

Publisher

ISBN-13 9781800200944

Pages 362 pages

Edition 2nd Edition

Languages

Concepts

Data Visualization

Authors (4):

Abha Belorkar

Sharath Chandra Guntuku

Shubhangi Hora

Anshu Kumar

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

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

Types of Temporal Data

Temporal data can contain information about the following:

Events: An event is a change in the state of an object at a given time. Event = Time + Object State. Examples of events are posting a tweet, sending an email, or sending a message.
Temporal information in tweets helps us understand trending topics, get the latest news updates, and analyze the sentiment of topics over time.
Measurements: Measurements records values across time. Measurement = Time + Measures. Examples of measurements are sensor data, revenue, and stock values.
Temporal measurement information is the key feature of time-series forecasting. Also, it helps us find patterns and anomalies in a dataset with sensor data.

Another view of time can be based on how it progresses:

Sequential: We consider time as continuous linear values here. An example of this type is a Unix timestamp.
Cyclical: Time can be viewed as a recurrent event, where it is understood as fixed...

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Authors (4)

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.

See other products by Abha Belorkar

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

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

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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