#### Overview of this book

Today's world of science and technology is all about speed and flexibility. When it comes to scientific computing, NumPy is on the top of the list. NumPy will give you both speed and high productivity. "NumPy Cookbook" will teach you all about NumPy, a leading scientific computing library. NumPy replaces a lot of the functionality of Matlab and Mathematica, but in contrast to those products, it is free and open source. "Numpy Cookbook" will teach you to write readable, efficient, and fast code that is as close to the language of Mathematics as much as possible with the cutting edge open source NumPy software library. You will learn about installing and using NumPy and related concepts. At the end of the book, we will explore related scientific computing projects. This book will give you a solid foundation in NumPy arrays and universal functions. You will also learn about plotting with Matplotlib and the related SciPy project through examples. "NumPy Cookbook" will help you to be productive with NumPy and write clean and fast code.
NumPy Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
Winding Along with IPython
Get to Grips with Commonly Used Functions
Connecting NumPy with the Rest of the World
Audio and Image Processing
Special Arrays and Universal Functions
Profiling and Debugging
Quality Assurance
Speed Up Code with Cython
Index

## Debugging with IPython

Debugging is one of those things nobody really likes, but is very important to master. It can take hours, and because of Murphy's law, you most likely, don't have that time. Therefore, it is important to be systematic and know your tools well. After you are done finding the bug and implementing a fix, you should have a test in place. This way at least you will not have to go through the hell of debugging again. Unit testing is covered in the next chapter. We will debug the following buggy code, which tries to access an array element that is not present:

```import numpy

a = numpy.arange(7)
print a[8]```

The IPython debugger works as the normal Python `pdb` debugger; it adds features such as tab completion and syntax highlighting.

### How to do it...

The following steps illustrate a typical debugging session:

1. Run the buggy script in IPython.

Start the IPython shell. Run the buggy script in IPython by issuing the following command:

```In [1]: %run buggy.py
------------------------------...```