Learning SciPy for Numerical and Scientific Computing

by Francisco J. Blanco-Silva (Author)

14 ratings

ISBN-13: 978-1782161622

ISBN-10: 1782161627

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Learning SciPy for Numerical and Scientific Computing

Publisher ‏ : ‎ Packt Publishing (February 22, 2013)
Language ‏ : ‎ English
Paperback ‏ : ‎ 150 pages
ISBN-10 ‏ : ‎ 1782161627
ISBN-13 ‏ : ‎ 978-1782161622
Item Weight ‏ : ‎ 12 ounces
Dimensions ‏ : ‎ 7.5 x 0.34 x 9.25 inches

Best Sellers Rank: #3,254,416 in Books (See Top 100 in Books)
- #1,383 in Mathematical & Statistical Software
- #6,016 in Software Development (Books)
- #32,087 in Computer Science (Books)

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14 ratings

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James Leibert

Very bad quality!

Reviewed in the United States on February 5, 2014

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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.

17 people found this helpful

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James Brownlow

Four Stars

Reviewed in the United States on June 13, 2015

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short book, I picked up a couple of tips for python. Not an exhaustive study by any means.

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Customer in NY

Where is the code for this book?

Reviewed in the United States on January 16, 2014

Verified Purchase

I will update this review based on the book's quality soon. However, I am disappointed that I am unable to download the code. If anyone knows the link pls post. Thanks folks.

EDIT: Publisher did get back to me. This is a quote: "Unfortunately we do not have code files for the book "Learning SciPy for Numerical and Scientific Computing" as of now. As soon as the author updates the code files you can download it from our website."

Dear authors,
Pls provide example source code. For me, having example code that accompanies the book is a key feature. Thanks ...

4 people found this helpful

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Eugene Morozov

The book is already obsolete

Reviewed in the United States on June 4, 2013

Verified Purchase

Most of examples could be simply find over the Internet.
I find stackoverflow much better source of knowledge, than such a book... unfortunately

3 people found this helpful

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Sujit Pal

Good overview of SciPy for Scientific Programmers

Reviewed in the United States on July 2, 2014

SciPy is Python's scientific library, commonly used with NumPy (the matrix library) and Matplotlib (the plotting library). The book opens with the obligatory chapter about installing the software and a refresher on NumPy. It then goes on to give a good overview of SciPy's capabilities in Linear Algebra (various decompositions, solvers, etc), Numerical methods (interpolation, regression, optimization, differentiation, integration, etc), Signal Processing (FFT, filters, etc), Image Manipulation (affines), Data Mining (curve fitting, clustering, spatial distance measures) and Computational Geometry. Each chapter roughly corresponds to functionality provided by one or two SciPy packages. The general approach is to describe a problem, demonstrate a solution using one of the approaches, then list a variety of other algorithms/methods that are also provided by SciPy. It closes with details of integrating SciPy with other languages (F77, C/C++, MATLAB/Octave). As expected, the examples also make use of NumPy and Matplotlib. Since each of these subject areas require significant domain expertise, the book makes the (very reasonable IMO) assumption that the reader has sufficient background in scientific computing and is only interested in what SciPy provides in their chosen area. I found Chapters 3 (Linear Algebra) and 6 (Data Mining) most useful, although Chapters 4 (Numerical Methods) and 5 (Signal Processing) provided me with subjects for further exploration. Surprisingly, the book does not come with downloadable code samples - it would have been more useful if it did.

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Parsa

Great for scientists, engineers, programmers and data analysts

Reviewed in the United States on April 27, 2013

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.

12 people found this helpful

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Linux User

Nice book. The book does a good job in ...

Reviewed in the United Kingdom on December 21, 2014

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Nice book. The book does a good job in going over some useful modules in Python,
I found the book entertaining rather than useful. I am a mathematician and have delved into the
book on occasion to familiarise myself with the functionality that Python can offer.

If I was serious about using any of the book-mentioned modules I would indeed use the online Python documentation.

Personally I wouldn't pay more than £5 for this book because it has relatively few pages and brief overviews in each chapter.

Marco

Nice book

Reviewed in Spain on April 22, 2013

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Clear, concise and well written. The examples are well selected and explained step by step. Check it out, worth it.

chevy

Indispensable

Reviewed in France on May 19, 2013

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Ce livre m'a permis d'abord d'installer SciPy (en NumPy) avec succès, puis d'en comprendre des bases. Il est à la fois assez simple et assez complet pour une bonne initiation au module.

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Learning SciPy for Numerical and Scientific Computing

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