Book Image

Matplotlib for Python Developers - Second Edition

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

Matplotlib for Python Developers - Second Edition

By: Aldrin Yim, Claire Chung, Allen Yu

Overview of this book

Python is a general-purpose programming language increasingly being used for data analysis and visualization. Matplotlib is a popular data visualization package in Python used to design effective plots and graphs. This is a practical, hands-on resource to help you visualize data with Python using the Matplotlib library. Matplotlib for Python Developers, Second Edition shows you how to create attractive graphs, charts, and plots using Matplotlib. You will also get a quick introduction to third-party packages, Seaborn, Pandas, Basemap, and Geopandas, and learn how to use them with Matplotlib. After that, you’ll embed and customize your plots in third-party tools such as GTK+3, Qt 5, and wxWidgets. You’ll also be able to tweak the look and feel of your visualization with the help of practical examples provided in this book. Further on, you’ll explore Matplotlib 2.1.x on the web, from a cloud-based platform using third-party packages such as Django. Finally, you will integrate interactive, real-time visualization techniques into your current workflow with the help of practical real-world examples. By the end of this book, you’ll be thoroughly comfortable with using the popular Python data visualization library Matplotlib 2.1.x and leveraging its power to build attractive, insightful, and powerful visualizations.
Table of Contents (16 chapters)
Title Page
Dedication
Packt Upsell
Contributors
Preface
Index

Expanding plot types with Seaborn 


To install the Seaborn package, we open the terminal or command prompt and call pip3 install --user seaborn. For each use, we import the library by import seaborn as sns, where sns is a commonly used shorthand to save typing.

Visualizing multivariate data with a heatmap

A heatmap is a useful visualization method to illustrate multivariate data when there are many variables to compare, such as in a big data analysis. It is a plot that displays values in a color scale in a grid. It is among the most common plots utilized by bioinformaticians to display hundreds or thousands of gene expression values in one plot.

With Seaborn, drawing a heatmap is just one line away from importing the library. It is done by calling sns.heatmap(df), where df is the Pandas DataFrame to be plotted. We can supply the cmap parameter to specify the color scale ("colormap") to be used. You can revisit the previous chapter for more details on colormap usage.

To get a feel for heatmap...