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Guide to NumPy: 2nd Edition 2nd Edition
Purchase options and add-ons
- ISBN-10151730007X
- ISBN-13978-1517300074
- Edition2nd
- Publication dateSeptember 15, 2015
- LanguageEnglish
- Dimensions7 x 0.82 x 10 inches
- Print length364 pages
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Product details
- Publisher : CreateSpace Independent Publishing Platform; 2nd edition (September 15, 2015)
- Language : English
- Paperback : 364 pages
- ISBN-10 : 151730007X
- ISBN-13 : 978-1517300074
- Item Weight : 1.4 pounds
- Dimensions : 7 x 0.82 x 10 inches
- Best Sellers Rank: #1,352,363 in Books (See Top 100 in Books)
- #1,082 in Computer Programming Languages
- #1,437 in Python Programming
- Customer Reviews:
About the author
Travis has a Ph.D. from the Mayo Clinic and B.S. and M.S. degrees in Mathematics and Electrical Engineering from Brigham Young University. Since 1997, he has worked extensively with Python for numerical and scientific programming, most notably as the primary developer of the NumPy package, and as a founding contributor of the SciPy package. He is also the author of the definitive "Guide to NumPy".
Travis was an assistant professor of Electrical and Computer Engineering at BYU from 2001-2007, where he taught courses in probability theory, electromagnetics, inverse problems, and signal processing. He also served as Director of the Biomedical Imaging Lab, where he researched satellite remote sensing, MRI, ultrasound, elastography, and scanning impedance imaging.
As CEO of Continuum Analytics, Travis engages customers in all industries, develops business strategy, and helps guide technical direction of the company. He actively contributes to software development and engages with the wider open source community in the Python ecosystem. He has served as a director of the Python Software Foundation and as a director of Numfocus.
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- Reviewed in the United States on January 6, 2018Important classic for a sense of what the developer of numpy was thinking about
- Reviewed in the United States on November 30, 2015I read the first edition of "Guide to Numpy" in 2006, at the suggestion of Paul Dubois, whose role in the story of Numpy is described in this book. It was the description of the Numpy C-API in this book that both solved a real problem for me at the time and got me hooked on using Numpy for scientific computing.
This second edition is a worthy update, and should probably sit within reach for any serious Numpy user. Some parts of the book (e.g. chapters 5 and 6) are more like a reference, but other parts offer a nice tour of available techniques and libraries for how to solve a particular problem. For example, Ch 14 "Using Python as Glue" is a well rounded chapter on the myriad choices one has in interfacing Python with compiled code. Reading through the ufunc section is rewarding, and I also found the testing section quite enlightening - definitely worth a read if you are like me, and were pretty much just using "np.assert_array_almost_equal" all over the place.
The C-API section is as useful as ever, with some nice tips on how to navigate Python's C-API and survive reference counting (relatively) unscathed.
The last chapter, "Code Explanations" ends abruptly and could have gone into more depth. Nevertheless, it's a reasonable 'brain dump' of how a lot of Numpy code came together and why it looks the way it does.
Disclosure: This book got me so interested in using Python/Numpy for scientific/mathematical computing that I continued to work in that area for many years, culminating in me joining Continuum Analytics, the company co-founded by Travis Oliphant, which is where I'm currently employed. My thoughts here are my own.
- Reviewed in the United States on December 30, 2016This Modern Control Theory book is the 1974 First Edition, not the Third Edition book like the picture on your order screen depicts. It's also in terrible condition, dog eared with handwriting and yellow highlighter throughout. Book is completely unusable and NOT what was "marketed". Don't waste your time and money.
- Reviewed in the United States on January 8, 2016This book is for scientists, engineers, and software developers who are familiar with basic NumPy usage and want to move on to the level of advanced users. It explains the design principles behind NumPy, such as the data types and memory layout of arrays and the all-important ufuncs, the "universal functions" which can be applied efficiently to arrays. It also explains how NumPy works at the C level, an important topic for those who write interfaces to C, C++, or Fortran libraries. Interfacing tools such as Cython, f2py, or SWIG are covered as well. Finally, there are lots of hints for doing computations efficiently based on a better understanding of how NumPy actually works.
I'd suggest readers to start reading chapters 1 to 3 in order. Then select from the following chapters by interest or need, and try to put the freshly learned material to some practical application before moving on to the next chapter. Don't try to read this book from cover to cover, as there is a serious risk of information overload.
This is the most in-depth book about NumPy I know of, written by the person who actually wrote most of the code. His profound understanding of NumPy shows through everywhere. Those looking for a beginner's level tutorial should look elsewhere, but for everyone else, this is the book you should have within reach from your keyboard.
- Reviewed in the United States on December 22, 2015These comments are really addressed to anyone new to Python and numpy. With such a crowded field of books about numpy due to it's incredible success, it's important to reiterate who Travis is and why you should read his book in particular. Simply put, Travis is not someone writing about numpy. Over-simplifying a little, Travis is the person who wrote numpy, more-or-less a Guido or Linus or Brian or Dennis of Python numerical computing. Hence, this is a book unlike any other, written by an author whose leadership and contributions stand out above all the others. If you've ever written C-code, you probably have a copy of K&R. Likewise, if you're getting into numpy, you should have a copy of this book; it has a unique perspective which simply cannot be found anywhere else.
- Reviewed in the United States on February 2, 2016I write Python code on a daily basis and often use the Pandas data manipulation library. This book provided a useful insight into the underlying NumPy framework, especially in the first two chapters. The first chapter gave me an appreciation for how NumPy evolved over the past two decades and its relation to newer additions in the Python ecospace, like Jupyter. The second chapter gave a clear explanation of how NumPy is based on two fundamental objects: N-dimensional arrays and universal functions. The remainder of the book went into great detail about every aspect of the library, with tips and examples scattered throughout. This book will be a useful resource as I further explore the numerical capabilities of Python.