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Mastering Matplotlib

Mastering Matplotlib

By : Duncan M. McGreggor
3.5 (8)
Buy this Book
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Mastering Matplotlib

Mastering Matplotlib

3.5 (8)
By: Duncan M. McGreggor
Buy this Book

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
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Getting Up to Speed
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1. Getting Up to Speed
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A brief historical overview of matplotlib
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What's new in matplotlib 1.4
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The intermediate matplotlib user
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Prerequisites for this book
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Python 3
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Coding style
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Installing matplotlib
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Using IPython Notebooks with matplotlib
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Advanced plots – a preview
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Setting up the interactive backend
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Summary
2
2. The matplotlib Architecture
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2. The matplotlib Architecture
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The original design goals
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The current matplotlib architecture
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The backend layer
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The artist layer
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The scripting layer
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The supporting components of the matplotlib stack
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matplotlib modules
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The execution flow
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The matplotlib architecture as it relates to this book
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Summary
3
3. matplotlib APIs and Integrations
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3. matplotlib APIs and Integrations
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The procedural pylab API
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The pyplot scripting API
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The matplotlib object-oriented API
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matplotlib in other frameworks
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Summary
4
4. Event Handling and Interactive Plots
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4. Event Handling and Interactive Plots
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Event loops in matplotlib
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Event handling
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Summary
5
5. High-level Plotting and Data Analysis
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5. High-level Plotting and Data Analysis
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High-level plotting
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Data analysis
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Summary
6
6. Customization and Configuration
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6. Customization and Configuration
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Customization
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Configuration
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Summary
7
7. Deploying matplotlib in Cloud Environments
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7. Deploying matplotlib in Cloud Environments
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Making a use case for matplotlib in the Cloud
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An example – AWS and Docker
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Summary
8
8. matplotlib and Big Data
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8. matplotlib and Big Data
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Big data
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Working with large data sources
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Visualizing large data
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Other visualization tools
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Summary
9
9. Clustering for matplotlib
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9. Clustering for matplotlib
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Clustering and parallel programming
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The custom ZeroMQ cluster
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Clustering with IPython
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More clustering
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Summary
10
Index
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Index

Chapter 5. High-level Plotting and Data Analysis

A significant aspect of gaining matplotlib mastery is familiarizing oneself with the use of the other Python tools in the scientific programming ecosystem. Libraries such as NumPy, SciPy, Pandas, or SymPy are just the beginning. The tools available in the community cover an enormous amount of ground, entailing the spectrum of many fields and subspecialties. Projects such as scikit-learn, AstroPy, h5py, and so on, build upon the foundations provided by others, thus being able to provide more functionality quicker than, if they had to start from scratch themselves.

Those who may want to look more deeply into these and other tools may benefit from a guided tour into one area that could serve as a template for future exploration into many other areas. This is our mission in this chapter, with our entry points for further examination being the following:

  • A background and overview of high-level plotting
  • Practical high-level plotting, using...
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Mastering Matplotlib
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