Contents and structure
The organization and flow are good. The ten chapters contain well thought out examples that you can use as building blocks for your scientific computing projects. Every example is structured in this way:
- An introduction to the problem that the example will solve.
- The code, commented line by line.
- The result of the code.
- A short recap of how the problem has been solved.
- And, sometimes, a multiple choice question to help the reader to test his own understanding.
There is no attempt at teaching the mathematics behind the examples. Every example is a "how to" that can help you to learn how to use the library and can save hours of searching through the official documentation and more complicated texts.
The chapters 1,2 and 3 contain the starting points to use NumPy. They explain how to install NumPy, how to handle the NumPy arrays and how to use some of the basic mathematical/statistical functions provided by the library. Chapters 4 through 7 cover the basics about handling matrices, how to load and write data, how to write universal functions and cover some of the basic modules that are discussed. Chapter 8 explains how to use the unit test functions provided by NumPy. Finally, chapters 9 and 10 (my favorites!) introduce how to integrate NumPy with Matplotlib and SciPy.
Who is this book for? This book is aimed at people who know Python and need to start using scientific computing in their programs. It is also suitable for people who use another scientific computing environment, such as Matlab, and want quick-start introduction to NumPy.