Mastering Matplotlib: A practical guide that takes you beyond the basics of matplotlib and gives solutions to plot complex data

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What you will learn

Analyze the matplotlib code base and its internals

Rerender visualized data on the fly based on changes in the user interface

Take advantage of sophisticated thirdparty 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 dashboardlike results

Deploy matplotlib in Cloud environments

Utilize matplotlib in big data projects

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Frequently bought together

Learning SciPy for Numerical and Scientific Computing Second Edition

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Amazon verified reviews

Justin Marley Nov 24, 2017

Very good book with a lot of material including code, graphs and hardware/cloud discussion to take the reader to the next level.

Amazon Verified review

Loris Aug 11, 2015

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.

Ro'eh Nävee May 12, 2017

'Mastering matplotlib' is a textbook for someone acquainted with using the Python library named 'matplotlib.' Also the IPython/Jupyter interpreter, since the 'nbagg' IPython Notebook backend to matplotlib will also be used. The 'nbagg' backend is for working with plots in a Web browser. Other Python libraries were SciPy, NumPy, Pandas and Seaborn.Most Jupyter notebooks in the tutorials start with:import matplotlibmatplotlib.use('nbagg')%matplotlib inlineimport matplotlib.pyplot as pltOne convenience to appreciate is how the author listed dependent libraries featured in the textbook, and even stored them in a text file included within the accompanying code, which can be downloaded for free from Packt Publishing. The generous author even states: "...you are welcome to...utilize the matplotlib library and the provided code in whatever way you see fit." Free practical, usable code. Once the free code has been downloaded, the github.com repo masteringmatplotlib/notebooks is optional. Mastering matplotlib

G. A. Patino Aug 12, 2015

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.

NRK Aug 14, 2018

I was hoping for a systematic understanding of the package. Didn't get it.

Mastering Matplotlib: A practical guide that takes you beyond the basics of matplotlib and gives solutions to plot complex data

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