What this book covers

Chapter 1, NumPy Quick Start, guides you through the steps needed to install NumPy on your system and create a basic NumPy application.

Chapter 2, Beginning with NumPy Fundamentals, introduces NumPy arrays and fundamentals.

Chapter 3, Getting Familiar with Commonly Used Functions, teaches you the most commonly used NumPy functions—the basic mathematical and statistical functions.

Chapter 4, Convenience Functions for Your Convenience, tells you about functions that make working with NumPy easier. This includes functions that select certain parts of your arrays, for instance, based on a Boolean condition. You also learn about polynomials and manipulating the shapes of NumPy objects.

Chapter 5, Working with Matrices and ufuncs, covers matrices and universal functions. Matrices are well-known in mathematics and have their representation in NumPy as well. Universal functions (ufuncs) work on arrays element by element, or on scalars. ufuncs expect a set of scalars as the input and produce a set of scalars as the output.

Chapter 6, Moving Further with NumPy Modules, discusses a number of basic modules of universal functions. These functions can typically be mapped to their mathematical counterparts, such as addition, subtraction, division, and multiplication.

Chapter 7, Peeking into Special Routines, describes some of the more specialized NumPy functions. As NumPy users, we sometimes find ourselves having special requirements. Fortunately, NumPy satisfies most of our needs.

Chapter 8, Assuring Quality with Testing, teaches you how to write NumPy unit tests.

Chapter 9, Plotting with matplotlib, covers matplotlib in depth, a very useful Python plotting library. NumPy cannot be used on its own to create graphs and plots. matplotlib integrates nicely with NumPy and has plotting capabilities comparable to MATLAB.

Chapter 10, When NumPy Is Not Enough – SciPy and Beyond, covers more details about SciPy. We know that SciPy and NumPy are historically related. SciPy, as mentioned in the History section, is a high-level Python scientific computing framework built on top of NumPy. It can be used in conjunction with NumPy.

Chapter 11, Playing with Pygame, is the dessert of this book. You learn how to create fun games with NumPy and Pygame. You also get a taste of artificial intelligence in this chapter.

Appendix A, Pop Quiz Answers, has the answers to all the pop quiz questions within the chapters.

Appendix B, Additional Online Resources, contains links to Python, mathematics, and statistics websites.

Appendix C, NumPy Functions' References, lists some useful NumPy functions and their descriptions.