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This is the second edition of Travis Oliphant's A Guide to NumPy originally published electronically in 2006. It is designed to be a reference that can be used by practitioners who are familiar with Python but want to learn more about NumPy and related tools. In this updated edition, new perspectives are shared as well as descriptions of new distributed processing tools in the ecosystem, and how Numba can be used to compile code using NumPy arrays. Travis Oliphant is the co-founder and CEO of Continuum Analytics. Continuum Analytics develops Anaconda, the leading modern open source analytics platform powered by Python. Travis, who is a passionate advocate of open source technology, has a Ph.D. from 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 computational and data science. He was the primary creator of the NumPy package and founding contributor to the SciPy package. He was also a co-founder and past board member of NumFOCUS, a non-profit for reproducible and accessible science that supports the PyData stack. He also served on the board of the Python Software Foundation.
- Sales Rank: #227641 in Books
- Published on: 2015-09-15
- Original language: English
- Number of items: 1
- Dimensions: 10.00" h x .82" w x 7.00" l, 1.40 pounds
- Binding: Paperback
- 364 pages
About the Author
Travis E. Oliphant, a passionate advocate of open source technology, has a Ph.D. from 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. He is the primary developer of the NumPy package and founding contributor to the SciPy package.
Most helpful customer reviews
5 of 5 people found the following review helpful.
A nice update to the original classic
By Tyler J. Alumbaugh
I 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.
4 of 4 people found the following review helpful.
Your guide to advanced NumPy
By Konrad Hinsen
This 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.
1 of 1 people found the following review helpful.
Comprehensive reference for NumPy
By Naveen N Sinha
I 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.
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