Mastering matplotlib

图书信息

作者	Duncan M. McGreggor
出版社	Packt Publishing
ISBN	9781783987559
出版时间	2015-06-29
字数	212.0万
分类	进口书,外文原版书,电脑,网络

读书简介

If you are a scientist, programmer, software engineer, or student who has working knowledge of matplotlib and now want to extend your usage of matplotlib to plot complex graphs and charts and handle large datasets, then this book is for you.

目录

Mastering matplotlib

Table of Contents

Mastering matplotlib

Credits

About the Author

About the Reviewers

www.PacktPub.com

Support files, eBooks, discount offers, and more

Why subscribe?

Free access for Packt account holders

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

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

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

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

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

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

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

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

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 the 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

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

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