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  • Book Overview & Buying Mastering Matplotlib
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Mastering Matplotlib

Mastering Matplotlib

By : Duncan M. McGreggor
3.5 (8)
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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
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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
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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

matplotlib in other frameworks

In general, third-party libraries that depend on or provide support to the process of interfacing with matplotlib do so mostly through the scripting layer. Here is a quick summary of a few libraries that use matplotlib:

  • Seaborn: This library uses all the matplotlib.pyplot functions.
  • NetworkX: This library essentially uses all the matplotlib.pyplot functions, with the only exception being some backend and artist layer interfacing that is used to generate the project's gallery (which is another good example of the programmatic approach for batched jobs).
  • Pandas: This library mostly uses matplotlib.pyplot, but it sometimes also uses the deprecated pylab interface. It also has some custom transforms and tickers in its time series converter code.
  • scikit-learn: This library mostly uses matplotlib.pyplot along with some custom use of the font manager and color mapping. It also takes advantage of the collections, finance, and mpl_toolkits modules, among others. Additionally...
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Mastering Matplotlib
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