Book Image

Interactive Data Visualization with Python - Second Edition

By : Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, Anshu Kumar
Book Image

Interactive Data Visualization with Python - Second Edition

By: Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, Anshu Kumar

Overview of this book

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.
Table of Contents (9 chapters)

Data Formatting and Interpretation

The purpose of interactive data visualization is to visually and interactively present data so that it is easy to comprehend. Thus, naturally, data is the most important factor of any visualization. Hence, the first phase of data visualization is understanding the data in front of you – understanding what it is, what it means, and what it's conveying. Only when you understand the data will you be able to design a visualization that will help others understand it.

Additionally, it is important to ensure that your data makes sense and contains enough information – be it categorical, numerical, or a mix of both – to be visualized. So, if you are dealing with erroneous or dirty data, you're bound to end up with a faulty visualization.

In the next section, we'll look at a few ways to avoid common mistakes that are typically made in this phase of data and how to avoid them.

Avoiding Common Pitfalls while Dealing...