Contents and structureThe 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.
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.