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 Visualization

The actual visualization is as important as the data that is being visualized, obviously, since it is the end product of the process. Thus, paying close attention to creating the best possible visualization for the data at hand is crucial.

Interactive visualizations have multiple elements/parts. Let's take a closer look at each element to understand what can go wrong and how to prevent such mistakes.

Choosing a Visualization

Once your data has been cleaned and prepared, and the features that you want to visualize have been chosen, the first step in creating a visualization is selecting the graph or plot that is going to display your data. This decision impacts the efficiency and ease with which your visualization can explain your data, and thus you need to ensure that you're picking a visualization that can accurately explain and describe your data.

In the previous chapters, we looked at three types of data – stratified, temporal...