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Mastering matplotlib, by Duncan M. McGreggor
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A practical guide that takes you beyond the basics of matplotlib and gives solutions to plot complex data
About This Book- Customize, configure, and handle events, and interact with figures using matplotlib
- Create highly intricate and complicated graphs using matplotlib
- Explore matplotlib's depths through examples and explanations in IPython notebooks
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.
What You Will Learn- Analyze the matplotlib code base and its internals
- Re-render visualized data on the fly based on changes in the user interface
- Take advantage of sophisticated third-party libraries to plot complex data relationships
- Create custom styles for use in specialize publications, presentations, or online media
- Generate consolidated master plots comprising many subplots for dashboard-like results
- Deploy matplotlib in Cloud environments
- Utilize matplotlib in big data projects
matplotlib is a Python plotting library that provides a large feature set for a multitude of platforms. Given the depth of the library's legacy and the variety of related open source projects, gaining expert knowledge can be a time-consuming and often confusing process.
You'll begin your exciting journey learning about the skills that are necessary in leading technical teams for a visualization project or to become a matplotlib contributor.
Supported by highly-detailed IPython Notebooks, this book takes you through the conceptual components underlying the library and then provides a detailed overview of its APIs. From there, you will learn about event handling and how to code for interactive plots.
Next you will move on to customization techniques, local configuration of matplotib, and then deployments in Cloud environments. The adventure culminates in an exploration of big data visualization and matplotlib clustering.
- Sales Rank: #394570 in Books
- Published on: 2015-06-04
- Released on: 2015-06-29
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .66" w x 7.50" l, 1.11 pounds
- Binding: Paperback
- 314 pages
About the Author
Duncan M. McGreggor
Duncan M. McGreggor, having programmed with GOTOs in the 1980s, has made up for that through community service by making open source contributions for more than 20 years. He has spent a major part of the past 10 years dealing with distributed and scientific computing (in languages ranging from Python, Common Lisp, and Julia to Clojure and Lisp Flavored Erlang). In the 1990s, after serving as a linguist in the US Army, he spent considerable time working on projects related to MATLAB and Mathematica, which was a part of his physics and maths studies at the university. Since the mid 2000s, matplotlib and NumPy have figured prominently in many of the interesting problems that he has solved for his customers. With the most recent addition of the IPython Notebook, matplotlib and the suite of the Python scientific computing libraries remain some of his most important professional tools.
Most helpful customer reviews
8 of 8 people found the following review helpful.
The matplotlib book for intermediate users
By G. A. Patino
This is the definite book to learn the most advanced aspects of matplotlib. Even though the book covers installation and gives the fundamentals about basic plots, it is best suited for intermediate users in both Python and matplotlib. In fact, the authors are very class-oriented, making a fair familiarity with object-oriented programming something of a prerequisite. However, if you have those prerequisites the book is a worthy investment as you will be able to take full advantage of matplotlib.
Even though the topics covered are advanced, from the matplotlib architecture to deploying it in Docker and implementing in parallel computing, they are presented in a clear and concise way. Yet the breadth of applications covered is quite comprehensive, and the authors are able to articulate all the different chapters so that the learning feels like a natural progression instead of trying to cram very disparate subjects. The code is elegant and relatively short, facilitating its reading; and the authors explanations for it are very easy to follow. In particular, the chapter of big data visualization definitely goes beyond what is presented in other books that also cover the same topic, and the implementation explanations are much better. The fact that the book is under 300 pages long is a huge plus. The only chapter I felt that wasn't as easy to follow is the one on GUI deployment.
One aspect I really enjoyed about the book is the multiple explanations about the different approaches to creating figures with matplotlib. When you are learning Python and matplotlib you see some books that use pyplot, while others use pylab. Or some like the ax. synthax while others stick to the plt. one. This is the first book in which I see a presentation of all those possibilities, along with their advantages and disadvantages. By the same token, if you are confused as to when to use Seaborn vs yhat ggplot, what's the point of NetworkX, what is ModGrapher, etc. you will find all those explanations here, along with suggestions for their appropriate application.
5 of 5 people found the following review helpful.
Very good book to improve your matplotlib skills
By Loris
I bought this book with the goal of improving my basic knowledge of matplotlib: while the creation of basic plots is straightforward, I found it difficult to modify the default behaviors of the library, and I faced problems in plotting large amount of data. I can safely say that this book helped me to solve both issues. As the author mentions in the book, a prior knowledge of matplotlib will definitely makes the reading more enjoiable.
The book begins by giving an historical overview of matplotlib and by introducing two popular projects, seaborn and pandas. The chapters that follow describes the matplotlib internal architecture and its API. Next the author illustrates how events are handled in matplotlib and how to create interactive plots. The fifth chapter is dedicated to high-level plotting and shows how to create plots with third-party libraries, such as networkX, pandas, and seaborn, which wrap matplotlib functionality. The chapter also briefly introduces Bokeh, a library that offers a series of improvements over matplotlib and focus its attention on the web browser. In this chapter the author did a very good job in showing how a good visualization of the data is crucial in data analysis. The next chapter covers the customization (and the configuration) of matplotlib. Here the author shows how to create complex layouts where different plots are combined in the same figure. In the eight chapter the author explains how to plot huge amount of data by illustrating different strategies that range from using tools such as numpy's memmap function and pytables, to decimating data (removal of a fraction of the data). The last chapter shows how it is possible to improve the performance of matplotlib by using a clustered environment.
Last but not least, the authors provided a GitHub repository with the example code and notebooks of each chapter of the book.
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