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NumPy 1.5 Beginner's Guide Paperback – November 8, 2011

3.9 out of 5 stars 8 customer reviews

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Editorial Reviews

About the Author

Ivan Idris has a degree in Experimental Physics and several certifications (SCJP, SCWCD and other). His graduation thesis had a strong emphasis on Applied Computer Science. After graduating Ivan worked for several companies as a Java developer, Datawarehouse developer and Test Analyst.

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Product Details

  • Paperback: 234 pages
  • Publisher: Packt Publishing (November 8, 2011)
  • Language: English
  • ISBN-10: 1849515301
  • ISBN-13: 978-1849515306
  • Product Dimensions: 7.5 x 0.5 x 9.2 inches
  • Shipping Weight: 14.4 ounces (View shipping rates and policies)
  • Average Customer Review: 3.9 out of 5 stars  See all reviews (8 customer reviews)
  • Amazon Best Sellers Rank: #2,241,233 in Books (See Top 100 in Books)

More About the Author

Ivan Idris was born in Bulgaria from Indonesian parents. He moved to the Netherlands in the 1990s, where he graduated from high school and got a MSc in Experimental Physics.

His graduation thesis had a strong emphasis on Applied Computer Science. After graduating he worked for several companies as Java Developer, Datawarehouse Developer and QA Analyst.

His main professional interests are Business Intelligence, Big Data and Cloud Computing. Ivan Idris enjoys writing clean testable code and interesting technical articles.

Customer Reviews

Top Customer Reviews

Format: Paperback
Disclaimer: I was sent a free copy of the book for review by Packt.

There are fairly few books about scientific Python (I am in the process of writing one myself). Hans Petter Langtangen's books with Springer provide a good introduction to Python, NumPy, and SciPy for computational science and physical applications, but they leave a lot to be desired for the budding data analyst or R or MATLAB refugee. That being said, I was excited to see that this particular book was being written.

This is decidedly a beginner's guide. It gives a gentle introduction to NumPy arrays and features by means of hands-on examples. It does a good job of illustrating array-oriented computing (including such ideas as vectorization and broadcasting) using examples that are primarily derived from financial applications. Visualization uses matplotlib, although matplotlib is not given a more direct treatment until later in the book. In addition to examples combining array operations and matplotlib plots, the author gives an overview of the other important areas of the library: random number generation, fourier transforms, polynomials, ufuncs, and the linear algebra module. Additionally, there is a chapter on unit testing with NumPy and a short chapter on SciPy and statsmodels.

The quality and value of the book is hurt by a number of things.

First: editing and technical content. There is quite a bit of poorly formatted code and inconsistencies throughout the book that will very likely confuse new users. The author ought to have done a bit more research into common practice and conventions generally accepted by the scientific Python community: for example, sometimes functions are written using "numpy.", other times they are written without the explicit module reference.
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Format: Paperback
NumPy 1.5 achieves the goal stated on its cover "Learn by doing: less theory, more results". In my opinion, it is an exciting introduction to the large numpy module. Many of the examples pertain to money: stock market analysis. I learned quite a bit even though I was quite familiar with numpy prior to reading.

From the basic additional functionality of arithmetic operating over all data at once, to advanced math of polynomials, fast Fourier transform, singular value decomposition, to visualization with graphics, NumPy 1.5 motivates the python programmer to install and use numpy. The book assumes facility with python. For instance, author Ivan Idris expects you to know how to examine directories and files with your operating system. He expects you to know to import datetime and sys as you read the book. Since these are included in the companion code it may help to browse these sources alongside the text. Frankly, I appreciated being treated as competent. The book does not cover all the available random distributions, special functions, optimizations for special matrices. Nor should it as an introduction to numpy. Ivan provides direction for your further investigation.

I jotted a few notes as I read:
> The numpy installation instructions were included for several operating systems. My installation on ubuntu was perfect;
> The author employed a helpful a method of frequent summaries and quizzes;
> In many instances multiple solutions were presented for a task;
> NumPy 1.5 treats broadcasting almost implicitly. In chapter 1 we see an_array**3.
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Format: Paperback
Due primarily to their relative popularity, there are far more books available for R than for Python, at least related to using the respective languages for data analysis and other numerical applications. There are fewer yet related to the various third-party packages that are available for Python. So, it was with more than a little excitement that I tracked down a copy of the NumPy 1.5 Beginner's Guide, authored by Ivan Idris.

One of the unavoidable issues with writing books about software is that for even moderately well-maintained packages, the release version has changed by the time the book is published; this is the case for Idris' book, as NumPy has reached version 1.6 (and many users work from the current codebase on GitHub). Don't let this deter you, however, as the major functionality changes little from version to version, particularly for point releases.

The book begins with thorough, visual installation instructions for the three major platforms (sorry, OS/2 users!). Though the NumPy website includes decent install instructions, this is a welcome chapter, particularly for new users, because it is well-organized and highly visual. Depending on your setup, the instructions for building from source may not be sufficient, at least on OS X, where some configuration is sometimes necessary depending on which version of Xcode (and hence, compilers) is installed.

In addition to installation guidance, the author reveals several avenues for 3rd-party help with NumPy, which is useful since one inevitably outstrips any book's ability to answer all one's questions. Its a good choice of recommended resources, too: mailing lists, IRC, and critically, Stack Overflow.
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