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Kommentar: 23,5 x 19,1 x 0,9 cm, Broschiert. Packt Publishing, 2013. 136 Seiten. Very good. With additional cover foliation. Note of ownership on front paper. ISBN: 9781782161622
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Learning SciPy for Numerical and Scientific Computing (Englisch) Taschenbuch – 22. Februar 2013

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Über den Autor und weitere Mitwirkende

Francisco J. Blanco-Silva

The owner of a scientific consulting company—Tizona Scientific Solutions—and adjunct faculty in the Department of Mathematics of the University of South Carolina, Dr. Blanco-Silva obtained his formal training as an applied mathematician at Purdue University. He enjoys problem solving, learning, and teaching. An avid programmer and blogger, when it comes to writing he relishes finding that common denominator among his passions and skills, and making it available to everyone.

He coauthored Chapter 5 of the book Modeling Nanoscale Imaging in Electron Microscopy, Springer by Peter Binev, Wolfgang Dahmen, and Thomas Vogt.

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Amazon.com: HASH(0xa64ade10) von 5 Sternen 11 Rezensionen
11 von 11 Kunden fanden die folgende Rezension hilfreich
HASH(0xa616c87c) von 5 Sternen Great for scientists, engineers, programmers and data analysts 27. April 2013
Von Parsa - Veröffentlicht auf Amazon.com
Format: Taschenbuch
This is a fantastic book for scientists, engineers, applied mathematicians, statisticians, programmers, and data analysts who have computation problems in mind and are looking to use an open-source programming language with plenty of modules to solve them. Python is my favorite high-level language because it's intuitive, very easy to install (if you own a Mac then you already have it!) and it has so many useful functions in the various module libraries.

My favorite thing about it this book is that when a module is introduced, the author gives a list of many relevant functions when appropriate. For example, when he introduces the linear algebra module (scipy.linalg) in Chapter 3, he goes through many of the matrix creation and operations functions that I didn't even know existed, and I'm an intermediate-level Python/NumPy user. He discusses solving large linear systems, eigenvalue problems, and FIVE different matrix decompositions as well as the corresponding module functions for each type of problem. This book is worth the price for Chapter 3 alone.

But thankfully, it goes on to discuss solving various common ODEs, optimization, the Runge-Kutta method, and numerical integration. And that's just Chapter 4. Again, the important detail here is how the author links each topic and problem to the corresponding SciPy module and relevant functions that do the vast majority of the work for you. He also shows how to use matplotlib for graphical purposes when a problem calls for it. Chapter 5 is about signal processing, which I didn't really understand but I think the gist of it is how to extrapolate from incomplete data and how to separate the signal from the noise.

I'm currently working as a data miner, which is the topic of Chapter 6. This is a nice introduction to the data analysis modules for SciPy: scipy.stats, scipy.spatial, and scipy.cluster. The data analysis examples were good, and the breakdown of hierarchical clustering was excellent, but I wished the chapter was a little longer. It is a great complement to McKinney's book on using Python for data analysis, which I also own.

All in all, I strongly recommend this book to anyone who has a computational problem to solve.
7 von 7 Kunden fanden die folgende Rezension hilfreich
HASH(0xa60b4c78) von 5 Sternen Great book for learning SciPy 21. Mai 2013
Von Scott MacLachlan - Veröffentlicht auf Amazon.com
Format: Taschenbuch
Learning SciPy for Numerical and Scientific Computing is a great reference book for mathematicians, scientists, engineers, and programmers looking to expand their computational toolbox. While matlab-based prototyping has, for many years, been the unchallenged standard in the development of computational algorithms, the development of the NumPy and SciPy packages in the last decade offers another option. This book focuses on introducing the syntax and capabilities of the combination of NumPy, SciPy, and matplotlib for standard problems in scientific computing. The book is built around numerous examples, with clearly explained source code and motivating discussions. While the material covered spans the range of a good numerical analysis textbook (linear algebra, interpolation, rootfinding, integration, ODEs, signal processing, data mining, computational geometry), the focus of this book is much more on the use of SciPy for these tasks than the development of the mathematics behind them or their use in large-scale simulations. Thus, the book is the perfect introduction to python's scientific computing abilities for a programmer already versed in numerical analysis and familiar with another programming language.
12 von 14 Kunden fanden die folgende Rezension hilfreich
HASH(0xa60b4210) von 5 Sternen Very bad quality! 5. Februar 2014
Von James Leibert - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
OK, this is in principle not a bad tour of some key SciPy functionality, but there are some serious problems with this book.

I'm writing this review after spending 3 hours with this book. I am so angry that I felt I needed to let other people know.

There are two major errors in the first two pieces of code in the book. If you are new to SciPy, as I was, that means wasting 2 hours ploughing through the SciPy online documentation to figure out the correct code (it is not easy!). Since the main reason for buying the book is that the online documentation makes absolutely no sense to newcomers, it rather defeats the purpose of the book.

So, being a good citizen, I did what was requested at the front of the book and attempted to submit an errata form with the correct code, or at least see what others had submitted, but the site has been abandoned by its owner.

I recommend you never buy a book from PACKT publishing, it is a complete rip off.

As to finding a good introduction to SciPy online or elsewhere, good luck, I'm still looking.
2 von 2 Kunden fanden die folgende Rezension hilfreich
HASH(0xa60b7b40) von 5 Sternen Good book for Scientific Developers 24. April 2013
Von Marcel - Veröffentlicht auf Amazon.com
Format: Taschenbuch
Overall the Learning Scipy for Numerical and Scientific Computing book is a good book on Scipy covering lots of mathematics with examples in Python. The book has a good size and it helps the scientists and scientific developers (by the way the non-developers will face some difficulties due to the heavy math that comes with the examples) to have a good overview on the library before exploring the reference material.

Further details at my website: [...]
1 von 1 Kunden fanden die folgende Rezension hilfreich
HASH(0xa60b8024) von 5 Sternen friendly, hands on, and very useful 22. April 2013
Von Ignacio Ramirez - Veröffentlicht auf Amazon.com
Format: Taschenbuch
This is a nice book for anyone working in scientific computing (or related areas such as applied mathematics, computer and electrical engineering, among others),
who aims to make Python his/her primary tool for developing and testing his/her algorithms. And this is in itself a very good idea, given the power and versatility of Python + NumPy + SciPy, and that they are free software.
The style of the book is clear, concise and easy to follow. Furthermore, it guides the reader through examples which are central in the practice of scientific computing, making these examples good starting points for the reader's own developments using Python.
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