Python is a general-purpose programming language that's increasingly being used for data analysis and visualization. Matplotlib is a popular data visualization package in Python that's used to design effective plots and graphs. This book is a practical, hands-on resource to help you visualize data with Python using the Matplotlib library.
This book shows you how to create attractive graphs, charts, and plots using Matplotlib. You will also get a quick introduction to the 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+, Qt 5, and WXWIDGETS.
You'll also be able to tweak the look and feel of your visualization with the help of the 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 Flask and 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 leverage its power to build attractive, insightful, and powerful visualizations.
This book is essentially for anyone who wants to create intuitive data visualizations using the Matplotlib library. If you're a data scientist or analyst and wish to create attractive visualizations using Python, you'll find this book useful. Some knowledge of Python programming is all you need to get started.
Chapter 1, Introduction to Matplotlib, gets you familiar with the capabilities and functionalities of Matplotlib.
Chapter 2, Getting Started with Matplotlib, gets you started with basic plotting techniques using Matplotlib syntax.
Chapter 3, Decorating Graphs with Plot Styles and Types, shows how to beautify your plots and select the right kind of plot that communicates your data effectively.
Chapter 4, Advanced Matplotlib, teaches you how to group multiple relevant plots into subplots in one figure using nonlinear scales, axis scales, plotting images, and advanced plots with the help of some popular third-party packages.
Chapter 5, Embedding Matplotlib in GTK+3, shows examples of embeding Matplotlib in applications using GTK+3.
Chapter 6,Embedding Matplotlib in Qt 5, explains how to embed a figure in a QWidget, use layout manager to pack a figure in a QWidget, create a timer, react to events, and update a Matplotlib graph accordingly. We use QT Designer to draw a simple GUI for Matplotlib embedding.
Chapter 7, Embedding Matplotlib in wxWidgets Using wxPython, shows how you can use Matplotlib in the wxWidgets framework, particularly using wxPython bindings.
Chapter 8, Integrating Matplotlib with Web Applications, teaches you how to develop a simple site that displays the price of Bitcoin.
Chapter 9, Matplotlib in the Real World, begins our journey of understanding more advanced Matplotlib usage through real-world examples.
Chapter 10, Integrating Data Visualization into the Workflow, covers a mini-project combining the skills of data analytics with the visualization techniques you have learned.
A working installation of Python 3.4 or later is required. The default Python distribution can be obtained from https://www.python.org/download/. The installation of packages is covered in the chapters, but you can refer to the official documentation pages for more details. A Windows 7+, macOS 10.10+, or Linux-based computer with 4 GB RAM or above is recommended.
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There are a number of text conventions used throughout this book.
CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Another parameter for tuning is dash_capstyle."
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import matplotlib.pyplot as plt plt.figure(figsize=(4,4)) x = [0.1,0.3] plt.pie(x) plt.show()
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