Packt publishing sent me a copy of NumPy 1.5 Beginner’s guide by Ivan Idris.
The book actually covers more than only numpy: it is a full introduction to numerical computing with Python. The table of contents is the following:
- NumPy Quick Start
- Beginning with NumPy Fundamentals
- Get into Terms with Commonly Used Functions
- Convenience Functions for Your Convenience
- Working with Matrices and ufuncs
- Move Further with NumPy Modules
- Peeking Into Special Routines
- Assure Quality with Testing
- Plotting with Matplotlib
- When NumPy is Not Enough: SciPy and Beyond
The book is easy to read, as it requires no specific expertise other than knowing basic Python programming. It is full of examples and exercises, which is really great for learning. I find the style of the author, Ivan Idris, particularly amusing and relaxing, engaging the reader with questions, challenges, or even jokes (“Have a go hero”).
With regards to the formatting and the print, the book is written in large fonts, with sectioning information, tips and exercises clearly standing out.
It is full of practical information, such as how to install the software, or where to get help. Finally, One thing that I appreciated, is that the examples are typed in IPython. Each time I teach, I like to use IPython, because it is full of features to help plotting, debugging and profiling numerical code. The book even has a little introduction to some useful IPython features.
After an introduction to the work flow, the book explores array manipulation such as creation or reshaping, followed by some simple numerics and the battery of array-based operations on functions and polynomials. Then it presents linear algebra and signal processing basics (FFT). It also covers the financial functions that are present in numpy and mentions testing, which is very important to achieve quality code. The book finishes with matplotlib and scipy, two modules that are important to know to go further.
The examples are mostly drawn from statistics or financial applications, such as computing running averages on stock quotes. Basic math explanations, such as the definition of the Moore-Penrose pseudo-inverse, are given when needed.
To conclude, I enjoyed this book and I think that it is a nice addition to my library. It answers exactly it’s title: it is well-suited for beginners wanting to learn numpy. On the other hand, I would not recommend it as a reference material, or as a book to learn more general scientific or numerical computing with Python.
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