Mastering matplotlib

Cover
Copyright
Credits
About the Author
About the Reviewers
www.PacktPub.com
Table of Contents
Preface
Chapter 1: Getting Up to Speed
	A brief historical overview of matplotlib
	What\'s new in matplotlib 1.4
	The intermediate matplotlib user
	Prerequisites for this book
	Python 3
	Coding style
	Installing matplotlib
	Using IPython Notebooks with matplotlib
	Advanced plots – a preview
	Setting up the interactive backend
		Joint plots with Seaborn
		Scatter plot matrix graphs with Pandas
	Summary
Chapter 2: The matplotlib Architecture
	The original design goals
	The current matplotlib architecture
	The backend layer
		FigureCanvasBase
		RendererBase
		Event
		Visualizing the backend layer
	The artist layer
		Primitives
		Containers
		Collections
		A view of the artist layer
	The scripting layer
	The supporting components of the matplotlib stack
	matplotlib modules
		Exploring the filesystem
		Exploring imports visually
			ModuleFinder
			ModGrapher
	The execution flow
		An overview of the script
		An interactive session
	The matplotlib architecture as it relates to this book
	Summary
Chapter 3: matplotlib APIs and Integrations
	The procedural pylab API
	The pyplot scripting API
	The matplotlib object-oriented API
		Equations
		Helper classes
		The Plotter class
		Running the jobs
	matplotlib in other frameworks
		An important note on IPython
	Summary
Chapter 4: Event Handling and Interactive Plots
	Event loops in matplotlib
		Event-based systems
		The event loop
		GUI toolkit main loops
		IPython Notebook event loops
		matplotlib event loops
	Event handling
		Mouse events
		Keyboard events
		Axes and figure events
		Object picking
		Compound event handling
			The navigation toolbar
			Specialized events
			Interactive panning and zooming
	Summary
Chapter 5: High-level Plotting and Data Analysis
	High-level plotting
		Historical background
		matplotlib
		NetworkX
		Pandas
		The grammar of graphics
		Bokeh
		The ŷhat ggplot
		New styles in matplotlib
		Seaborn
	Data analysis
		Pandas, SciPy, and Seaborn
		Examining and shaping a dataset
		Analysis of temperature
		Analysis of precipitation
	Summary
Chapter 6: Customization and Configuration
	Customization
		Creating a custom style
		Subplots
			Revisiting Pandas
			Individual plots
			Bringing everything together
		Further explorations in customization
	Configuration
		The run control for matplotlib
			File and directory locations
			Using the matplotlibrc file
			Updating the settings dynamically
		Options in IPython
	Summary
Chapter 7: Deploying matplotlib in Cloud Environments
	Making a use case for matplotlib in the Cloud
		The data source
		Defining a workflow
		Choosing technologies
			Configuration management
			The types of deployment
	An example – AWS and Docker
		Getting set up locally
			Requirements
		Dockerfiles and the Docker images
			Extending a Docker image
			Building a new image
		Preparing for deployment
		Getting setup on AWS
			Pushing the source data to S3
			Creating a host server on EC2
			Using Docker on EC2
			Reading and writing with S3
		Running the task
			Environment variables and Docker
			Changes to the Python module
			Execution
	Summary
Chapter 8: matplotlib and Big Data
	Big data
	Working with large data sources
		An example problem
		Big data on the filesystem
			NumPy\'s memmap function
			HDF5 and PyTables
		Distributed data
			MapReduce
			Open source options
			An example – working with data on EMR
	Visualizing large data
		Finding the limits of matplotlib
		Agg rendering with matplotlibrc
		Decimation
		Additional techniques
	Other visualization tools
	Summary
Chapter 9: Clustering for matplotlib
	Clustering and parallel programming
	The custom ZeroMQ cluster
		Estimating the value of π
		Creating the ZeroMQ components
		Working with the results
	Clustering with IPython
		Getting started
		The direct view
		The load-balanced view
		The parallel magic functions
		An example – estimating the value of π
	More clustering
	Summary
Index