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.

Table of Contents

1. Introduction to Visualization with Python-Basic and Customized Plotting
2. Static Visualization - Global Patterns and Summary Statistics
3. From Static to Dynamic Visualization
4. Interactive Visualization of Data across Strata
5. Interactive Visualization of Data across Time
6. Interactive Visualization of Data across Geographical Regions
7. Avoiding Common Pitfalls to Create Interactive Visualization

About the Author

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.

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.

Shubhangi Hora is a Python developer, artificial intelligence enthusiast, data scientist, and writer. With a background in computer science and psychology, she is particularly passionate about mental health-related AI. Apart from this, she is interested in the performing arts and is a trained musician.

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.