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Mastering Matplotlib

By : Duncan M. McGreggor

3.5 (8)

Mastering Matplotlib

3.5 (8)

By: Duncan M. McGreggor

Overview of this book

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.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Getting Up to Speed

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

Summary

2. The matplotlib Architecture

2. The matplotlib Architecture

The original design goals

The current matplotlib architecture

The backend layer

The artist layer

The scripting layer

The supporting components of the matplotlib stack

matplotlib modules

The execution flow

The matplotlib architecture as it relates to this book

Summary

3. matplotlib APIs and Integrations

3. matplotlib APIs and Integrations

The procedural pylab API

The pyplot scripting API

The matplotlib object-oriented API

matplotlib in other frameworks

Summary

4. Event Handling and Interactive Plots

4. Event Handling and Interactive Plots

Event loops in matplotlib

Event handling

Summary

5. High-level Plotting and Data Analysis

5. High-level Plotting and Data Analysis

High-level plotting

Data analysis

Summary

6. Customization and Configuration

6. Customization and Configuration

Customization

Configuration

Summary

7. Deploying matplotlib in Cloud Environments

7. Deploying matplotlib in Cloud Environments

Making a use case for matplotlib in the Cloud

An example – AWS and Docker

Summary

8. matplotlib and Big Data

8. matplotlib and Big Data

Big data

Working with large data sources

Visualizing large data

Other visualization tools

Summary

9. Clustering for matplotlib

9. Clustering for matplotlib

Clustering and parallel programming

The custom ZeroMQ cluster

Clustering with IPython

More clustering

Summary

Index

Index

Customer Reviews

3.5 (8)

5 star

37.5%

4 star

12.5%

3 star

25%

2 star

12.5%

1 star

12.5%

Summary

In this chapter, you got to learn a little more about matplotlib's origins and the latest features that were released at the time of writing this book. You've seen the software that we're going to use, including the version of the Python programming language that we've chosen. Furthermore, we've given you a peek into the future of this book (and matplotlib) with a custom IPython Notebook, which highlights the Seaborn and Pandas projects.

In the next couple of chapters, we're going to focus on matplotlib's internals. In particular, Chapter 2, The matplotlib Architecture will cover the architecture of the project, giving you an insight into how it all works together.

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Tech Concepts

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Programming languages

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