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)

Creating Plots that Present Global Patterns in Data

In this section, we will study the context of plots that present global patterns in data, such as:

  • Plots that show the variance in individual features in data, such as histograms
  • Plots that show how different features present in data vary with respect to each other, such as scatter plots, line plots, and heatmaps

Most data scientists prefer to see such plots because they give an idea of the entire spectrum of values taken by the features of interest. Plots depicting global patterns are also useful because they make it easier to spot anomalies in data.

We will work with a dataset called mpg. It was published by the StatLib library, maintained at Carnegie Mellon University, and is available in the seaborn library. It was originally used to study the relationship of mileage – Miles Per Gallon (MPG) – with other features in the dataset; hence the name mpg. Since the dataset contains 3 discrete features...