#### 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

## Profiling with IPython

In IPython, we can profile small snippets of code using `timeit`. We can also profile a larger script. We will show both approaches.

### How to do it...

First, we will time a small snippet.

1. Timing a snippet.

Start IPython in pylab mode:

`ipython -pylab`

Create an array containing 1000 integer values between 0 and 1000:

`In [1]: a = arange(1000)`

Measure the time taken for searching "the answer to everything"—42, in the array. Yes, the answer to everything is 42. If you don't believe me please read http://en.wikipedia.org/wiki/42_%28number%29.

```In [2]: %timeit searchsorted(a, 42)
100000 loops, best of 3: 7.58 us per loop```
2. Profile a script.

We will profile this small script that inverts a matrix of varying size containing random values. The `.I` attribute (that's uppercase I) of a NumPy array represents the inverse of a matrix:

```import numpy

def invert(n):
a = numpy.matrix(numpy.random.rand(n, n))
return a.I

sizes = 2 ** numpy.arange(0, 12)

for n in sizes:
invert(n)```

We can time this as...