#### 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 timeit

`timeit` is a module that allows you to time pieces of code. It is part of the standard Python library. We will time the NumPy `sort` function with several different array sizes. The classic quicksort and merge sort algorithms have an average running time of O(nlogn); so we will try to fit our result to such a model.

### How to do it...

We will require arrays to sort.

1. Create arrays to sort.

We will create arrays of varying sizes containing random integer values:

```times = numpy.array([])

for size in sizes:
integers = numpy.random.random_integers(1, 10 ** 6, size)```
2. Measure time.

In order to measure time, we need to create a timer and give it a function to execute and specify the relevant imports. After that, sort 100 times to get some data about the sorting times:

```def measure():
timer = timeit.Timer('dosort()', 'from __main__ import dosort')

return timer.timeit(10 ** 2)```
3. Build measurement time arrays.

Build the measurement time arrays by appending times one by one:

`times...`