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Mastering Matplotlib

Mastering Matplotlib

By : Duncan M. McGreggor, Duncan M McGreggor
3.5 (8)
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Mastering Matplotlib

Mastering Matplotlib

3.5 (8)
By: Duncan M. McGreggor, 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.
Table of Contents (11 chapters)
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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
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
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
3. matplotlib APIs and Integrations
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3. matplotlib APIs and Integrations
The procedural pylab API
The pyplot scripting API
The matplotlib object-oriented API
matplotlib in other frameworks
Summary
4
4. Event Handling and Interactive Plots
4. Event Handling and Interactive Plots
Event loops in matplotlib
Event handling
Summary
5
5. High-level Plotting and Data Analysis
5. High-level Plotting and Data Analysis
High-level plotting
Data analysis
Summary
6
6. Customization and Configuration
6. Customization and Configuration
Customization
Configuration
Summary
7
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
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
9. Clustering for matplotlib
9. Clustering for matplotlib
Clustering and parallel programming
The custom ZeroMQ cluster
Clustering with IPython
More clustering
Summary
10
Index
Index

The procedural pylab API

The pylab API is a procedural interface to matplotlib's underlying object-oriented Python API. As mentioned in the previous chapter and stated in the matplotlib FAQ, the pylab module has been deprecated. The pyplot module is the preferred API for scripting. The original vision of pylab—to provide MATLAB users a nearly one-to-one mapping of the software they knew with that of an open source platform—has been accomplished. In the 12 years since its inception, matplotlib has become a household name in scientific computing circles, and as a result the need for pylab has decreased greatly.

The drawbacks of pylab include the following:

  • It hides the workings (and thus, the deeper knowledge) of matplotlib, thus preventing the natural discovery of deeper feature sets that are available to the user through matplotlib objects.
  • The additional development and maintenance of pylab are a burden for the matplotlib team in its continued efforts to provide the MATLAB...
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