What this book covers

Chapter 1, Getting Up to Speed, covers some history and background of matplotlib, goes over some of the latest features of the library, provides a refresher on Python 3 and IPython Notebooks, and whets the reader's appetite with some advanced plotting examples.

Chapter 2, The matplotlib Architecture, reviews the original design goals of matplotlib and then proceeds to discuss its current architecture in detail, providing visualizations of the conceptual structure and relationships between the Python modules.

Chapter 3, matplotlib APIs and Integrations, walks the reader through the matplotlib APIs, adapting a single example accordingly, examines how third-party libraries are integrated with matplotlib, and gives migration advice to the advanced users of the deprecated pylab API.

Chapter 4, Event Handling and Interactive Plots, provides a review of the event-based systems, covers event loops in matplotlib and IPython, goes over a selection of matplotlib events, and shows how to take advantage of these to create interactive plots.

Chapter 5, High-level Plotting and Data Analysis, combines the interrelated topics, providing a historical background of plotting, a discussion on the grammar of graphics, and an overview of high-level plotting libraries. This is then put to use in a detailed analysis of weather-related data that spans 120 years.

Chapter 6, Customization and Configuration, covers the custom styles in matplotlib and the use of grid specs to create a dashboard effect with the combined plots. The lesser-known configuration options are also discussed with an eye to optimization.

Chapter 7, Deploying matplotlib in Cloud Environments, explores a use case for matplotlib in a remote deployment, which is followed by a detailed programmatic batch-job example using Docker and Amazon AWS.

Chapter 8, matplotlib and Big Data, provides detailed examples of working with large local data sets, as well as distributed ones, covering options such as numpy.memmap, HDF5, and Hadoop. Plots with millions of points will also be demonstrated.

Chapter 9, Clustering for matplotlib, introduces parallel programming and clusters that are designed for use with matplotlib, demonstrating how to distribute the parts of a problem and then assemble the results for analysis in matplotlib.