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Learning SciPy for Numerical and Scientific Computing Paperback – February 22, 2013


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Product Details

  • Paperback: 150 pages
  • Publisher: Packt Publishing (February 22, 2013)
  • Language: English
  • ISBN-10: 1782161627
  • ISBN-13: 978-1782161622
  • Product Dimensions: 0.3 x 7.4 x 9.1 inches
  • Shipping Weight: 15.5 ounces (View shipping rates and policies)
  • Average Customer Review: 3.3 out of 5 stars  See all reviews (10 customer reviews)
  • Amazon Best Sellers Rank: #560,354 in Books (See Top 100 in Books)

Editorial Reviews

About the Author

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.

Customer Reviews

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Most Helpful Customer Reviews

7 of 7 people found the following review helpful By Parsa on April 27, 2013
Format: Paperback
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.
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5 of 5 people found the following review helpful By Scott MacLachlan on May 21, 2013
Format: Paperback
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.
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1 of 1 people found the following review helpful By Marcel on April 24, 2013
Format: Paperback
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: [...]
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1 of 1 people found the following review helpful By Ignacio Ramirez on April 22, 2013
Format: Paperback
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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Format: Paperback
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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