Matplotlib for Python Developers: Effective techniques for data visualization with Python

✍ Scribed by Yim, Aldrin;Chung, Claire;Yu, Allen

• Publisher: Packt Publishing
• Year: 2018
• Tongue: English
• Leaves: 300
• Edition: 2
• Category: Library

✦ Synopsis

Leverage the power of Matplotlib to visualize and understand your data more effectivelyKey Features Perform effective data visualization with Matplotlib and get actionable insights from your data Design attractive graphs, charts, and 2D plots, and deploy them to the web Get the most out of Matplotlib in this practical guide with updated code and examples Book DescriptionPython 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.What you will learn Create 2D and 3D static plots such as bar charts, heat maps, and scatter plots Get acquainted with GTK+3, Qt5, and wxWidgets to understand the UI backend of Matplotlib Develop advanced static plots with third-party packages such as Pandas, GeoPandas, and Seaborn Create interactive plots with real-time updates Develop web-based, Matplotlib-powered graph visualizations with third-party packages such as Django Write data visualization code that is readily expandable on the cloud platform Who this book is forThis 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.Table of Contents Introduction to Matplotlib Getting Started with Matplotlib Decorating Graphs with Plot Styles and Types Advanced Matplotlib Embedding Matplotlib in GTK+3 Embedding Matplotlib in Qt 5 Embedding Matplotlib in wxWidgets Using wxPython Integrating Matplotlib with Web Applications Matplotlib in the Real World Integrating Data Visualization into the Workflow

✦ Table of Contents

Cover
Title Page
Copyright and Credits
Dedication
Packt Upsell
Contributors
Table of Contents
Preface
Chapter 1: Introduction to Matplotlib
What is Matplotlib?
Merits of Matplotlib
Easy to use
Diverse plot types
Hackable to the core (only when you want)
Open source and community support
What's new in Matplotlib 2.x?
Improved functionality and performance
Improved color conversion API and RGBA support
Improved image support
Faster text rendering
Change in the default animation codec
Changes in default styles
Matplotlib website and online documentation. Output formats and backends Static output formats
Raster images
Vector images
Setting up Matplotlib
Installing Python
Python installation for Windows
Python installation for macOS
Python installation for Linux
Installing Matplotlib
About the dependencies
Installing the pip Python package manager
Installing Matplotlib with pip
Setting up Jupyter Notebook
Starting a Jupyter Notebook session
Running Jupyter Notebook on a remote server
Editing and running code
Manipulating notebook kernel and cells
Embed your Matplotlib plots
Documenting in Markdown
Save your hard work! Interactive navigation toolbarConfiguring Matplotlib
Configuring within Python code
Reverting to default settings
Global setting via configuration rc file
Finding the rc configuration file
Editing the rc configuration file
Summary
Chapter 3: Decorating Graphs with Plot Styles and Types
Controlling the colors
Default color cycle
Single-lettered abbreviations for basic colors
Standard HTML color names
RGB or RGBA color code
Hexadecimal color code
Depth of grayscale
Colormaps
Creating custom colormaps
Line and marker styles
Marker styles
Choosing the shape of markers. Using custom characters as markersAdjusting marker sizes and colors
Fine-tuning marker styles with keyword arguments
Line styles
Color
Line thickness
Dash patterns
Designing a custom dash style
Cap styles
Spines
More native Matplotlib plot types
Choosing the right plot
Histogram
Bar plot
Setting bar plot properties
Drawing bar plots with error bars using multivariate data
Mean-and-error plots
Pie chart
Polar chart
Controlling radial and angular grids
Text and annotations
Adding text annotations
Font
Mathematical notations
Mathtext
LaTeX support
External text renderer.