#### 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
Advanced Indexing and Array Concepts
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

## Indexing with a list of locations

Let's use the `ix_` function to shuffle the Lena image. This function creates a mesh from multiple sequences.

### How to do it...

We will start by randomly shuffling the array indices:

1. Shuffle array indices.

Create a random indices array with the `shuffle` function of the `numpy.random` module:

```def shuffle_indices(size):
arr = numpy.arange(size)
numpy.random.shuffle(arr)

return arr```
2. Plot the shuffled indices:

`matplotlib.pyplot.imshow(lena[numpy.ix_(xindices, yindices)])`

What we get is a completely scrambled Lena image, as shown in the following screenshot:

The following is the complete code for the recipe:

```import scipy.misc
import matplotlib.pyplot
import numpy.random
import numpy.testing

# Load the Lena array
lena = scipy.misc.lena()
xmax = lena.shape[0]
ymax = lena.shape[1]

def shuffle_indices(size):
arr = numpy.arange(size)
numpy.random.shuffle(arr)

return arr
xindices = shuffle_indices(xmax)
numpy.testing.assert_equal(len(xindices), xmax)
yindices...```