Mastering Matplotlib 2.x: Effective Data Visualization techniques with Python

Understand and build beautiful and advanced plots with Matplotlib and Python

Key Features

• Practical guide with hands-on examples to design interactive plots
• Advanced techniques to constructing complex plots
• Explore 3D plotting and visualization using Jupyter Notebook

Book Description

In this book, you'll get hands-on with customizing your data plots with the help of Matplotlib. You'll start with customizing plots, making a handful of special-purpose plots, and building 3D plots. You'll explore non-trivial layouts, Pylab customization, and more about tile configuration. You'll be able to add text, put lines in plots, and also handle polygons, shapes, and annotations. Non-Cartesian and vector plots are exciting to construct, and you'll explore them further in this book. You'll delve into niche plots and visualize ordinal and tabular data. In this book, you'll be exploring 3D plotting, one of the best features when it comes to 3D data visualization, along with Jupyter Notebook, widgets, and creating movies for enhanced data representation. Geospatial plotting will also be explored. Finally, you'll learn how to create interactive plots with the help of Jupyter.

Learn expert techniques for effective data visualization using Matplotlib 3 and Python with our latest offering -- Matplotlib 3.0 Cookbook

What you will learn

• Deal with non-trivial and unusual plots
• Understanding Basemap methods
• Customize and represent data in 3D
• Construct Non-Cartesian and vector plots
• Design interactive plots using Jupyter Notebook
• Make movies for enhanced data representation

Who this book is for

This book is aimed at individuals who want to explore data visualization techniques. A basic knowledge of Matplotlib and Python is required.

Table of Contents

1. Heavy Customization
2. Drawing on Plots
3. Special Purpose Plots
4. 3D & Geospatial Plotting
5. Interactive Plotting

Benjamin Walter Keller is currently a PhD candidate at McMaster University and gained his BSc in physics with a minor in computer science from the University of Calgary in 2011. His current research involves numerical modeling of galaxy evolution over cosmological timescales. As an undergraduate at the U of C, he worked on stacking radio polarization to examine faint extragalactic sources. He also worked in the POSSUM Working Group 2 to determine the requirements for stacking applications for the Australian SKA Pathfinder (ASKAP) radio telescope. He is particularly interested in questions involving stellar feedback (supernovae, stellar winds, and so on) and its impact on galaxies and their surrounding intergalactic medium.