Book Image

Matplotlib 2.x By Example

By : Allen Yu, Claire Chung, Aldrin Yim
Book Image

Matplotlib 2.x By Example

By: Allen Yu, Claire Chung, Aldrin Yim

Overview of this book

Big data analytics are driving innovations in scientific research, digital marketing, policy-making and much more. Matplotlib offers simple but powerful plotting interface, versatile plot types and robust customization. Matplotlib 2.x By Example illustrates the methods and applications of various plot types through real world examples. It begins by giving readers the basic know-how on how to create and customize plots by Matplotlib. It further covers how to plot different types of economic data in the form of 2D and 3D graphs, which give insights from a deluge of data from public repositories, such as Quandl Finance. You will learn to visualize geographical data on maps and implement interactive charts. By the end of this book, you will become well versed with Matplotlib in your day-to-day work to perform advanced data visualization. This book will guide you to prepare high quality figures for manuscripts and presentations. You will learn to create intuitive info-graphics and reshaping your message crisply understandable.
Table of Contents (15 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback

Chapter 8. Exploratory Data Analytics and Infographics

Let the data speak for themselves.

This is a well-known quote to many data scientists in the field. However, it is often not trivial to capture the hidden characteristics or features in big data, and some exploratory data analysis must be done before we fully understand the dataset.

In this chapter, we aim to perform some exploratory data analysis on two datasets, using the techniques that we have discussed in previous chapters. Here is a brief outline of this chapter:

  • Visualizing categorical data
  • Visualizing geographical data
  • GeoPandas library
  • Working with images using the PIL library
  • Importing/transforming images
  • Multiple subplots
  • Heatmap
  • Survival graph

We assume that the readers are now comfortable with the use of pandas DataFrame as it will be heavily used in this chapter.

Readers should also be noted that most exploratory data analyses actually involve a significant amount of statistics, including dimension reduction approaches such as PCA...