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Learning SciPy for Numerical and Scientific Computing [Englisch] [Taschenbuch]

Francisco J. Blanco-Silva

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22. Februar 2013

For solving complex problems in mathematics, science, or engineering, SciPy is the solution. Building on your basic knowledge of Python, and using a wealth of examples from many scientific fields, this book is your expert tutor.


  • Perform complex operations with large matrices, including eigenvalue problems, matrix decompositions, or solution to large systems of equations.
  • Step-by-step examples to easily implement statistical analysis and data mining that rivals in performance any of the costly specialized software suites.
  • Plenty of examples of state-of-the-art research problems from all disciplines of science, that prove how simple, yet effective, is to provide solutions based on SciPy.

In Detail

It's essential to incorporate workflow data and code from various sources in order to create fast and effective algorithms to solve complex problems in science and engineering. Data is coming at us faster, dirtier, and at an ever increasing rate. There is no need to employ difficult-to-maintain code, or expensive mathematical engines to solve your numerical computations anymore. SciPy guarantees fast, accurate, and easy-to-code solutions to your numerical and scientific computing applications.

"Learning SciPy for Numerical and Scientific Computing" unveils secrets to some of the most critical mathematical and scientific computing problems and will play an instrumental role in supporting your research. The book will teach you how to quickly and efficiently use different modules and routines from the SciPy library to cover the vast scope of numerical mathematics with its simplistic practical approach that's easy to follow.

The book starts with a brief description of the SciPy libraries, showing practical demonstrations for acquiring and installing them on your system. This is followed by the second chapter which is a fun and fast-paced primer to array creation, manipulation, and problem-solving based on these techniques.

What you will learn from this book

  • Learn to store and manipulate large arrays of data in any dimension.
  • Accurately evaluate any mathematical function in any given dimension, as well as its integration, and solve systems of ordinary differential equations with ease.
  • Learn to deal with sparse data to perform any known interpolation, extrapolation, or regression scheme on it.
  • Perform statistical analysis, hypothesis test design and resolution, or data mining at high level, including clustering (hierarchical or through vector quantization), and learn to apply them to real-life problems.
  • Get to grips with signal processing — filtering audio, images, or video to extract information, features, or removing components.
  • Effectively learn about window functions, filters, spectral theory, LTY systems theory, morphological operations, and image interpolation.
  • Acquaint yourself with the power of distances, Delaunay triangulations, and Voronoi diagrams for computational geometry, and apply them to various engineering problems.
  • Wrap code in other languages directly into your SciPy-based workflow, as well as incorporating data written in proprietary format (audio or image, for example), or from other software suites like Matlab/Octave.


A step-by-step practical tutorial with plenty of examples on research-based problems from various areas of science, that prove how simple, yet effective, it is to provide solutions based on SciPy.

Wird oft zusammen gekauft

Learning SciPy for Numerical and Scientific Computing + Learning IPython for Interactive Computing and Data Visualization + NumPy Beginner's Guide  - Second Edition
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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: 3.3 von 5 Sternen  10 Rezensionen
8 von 8 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Great for scientists, engineers, programmers and data analysts 27. April 2013
Von Parsa - Veröffentlicht auf Amazon.com
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.
5 von 5 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen Great book for learning SciPy 21. Mai 2013
Von Scott MacLachlan - Veröffentlicht auf Amazon.com
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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4.0 von 5 Sternen Good book for Scientific Developers 24. April 2013
Von Marcel - Veröffentlicht auf Amazon.com
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
4.0 von 5 Sternen friendly, hands on, and very useful 22. April 2013
Von Ignacio Ramirez - Veröffentlicht auf Amazon.com
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.
4.0 von 5 Sternen Good overview of SciPy for Scientific Programmers 2. Juli 2014
Von Sujit Pal - Veröffentlicht auf Amazon.com
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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