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)

Summary

In this chapter, we presented three different types of visualization using geographical data choropleth maps, scatter plots and bubble plots on geographical maps, and line plots on geographical maps. Choropleth maps present aggregate statistics across different regions on geographical maps. Scatter plots are effective at indicating details regarding specific locations of interest, whereas bubble plots are useful for presenting count data per region on a map. Line plots are helpful in visualizing the routes of transportation systems, for instance.

These plots can easily be generated using the plotly express and graph_objects modules. Animation can be performed with respect to a discrete numeric feature in a dataset.

In the next chapter, we'll look at a few common pitfalls faced while creating visualizations and how to avoid them. Along with that, we'll also look at a cheat sheet for generating interactive visualizations.