Book Image

IPython Notebook Essentials

By : Luiz Felipe Martins
Book Image

IPython Notebook Essentials

By: Luiz Felipe Martins

Overview of this book

Table of Contents (15 chapters)
IPython Notebook Essentials
About the Author
About the Reviewers

Indexing and Slicing

To illustrate indexing, let's first create an array with random data using the following command:

import numpy.random
a = np.random.rand(6,5)
print a

This creates an array of dimension (6,5) that contains random data. Individual elements of the array are accessed with the usual index notation, for example, a[2,4].

An important technique to manipulate data in NumPy is the use of slices. A slice can be thought of as a subarray of an array. For example, let's say we want to extract a subarray with the middle two rows and first two columns of the array a. Consider the following command lines:

b = a[2:4,0:2]
print b

Now, let's make a very important observation. A slice is simply a view of an array, and no data is actually copied. This can be seen by running the following commands:

print a

So, changes in b affect the array a! If we really need a copy, we need to explicitly say we want one. This can be done using the following command line:

c = np.copy(a[2:4,0:2])
c[0,0] = -1
print a

In the slice notation i:j, we can omit either i or j, in which case the slice refers to the beginning or end of the corresponding axis:

print a[:4,3:]

Omitting both i and j refers to a whole axis:

print a[:,2:4]

Finally, we can use the notation i:j:k to specify a stride k in the slice. In the following example, we first create a larger random array to illustrate this:

a = np.random.rand(10,6)
print a
print a[1:7:2,5:0:-3]

Let's now consider slices of higher dimensional arrays. We will start by creating a really large three-dimensional array as follows:

d1, d2, d3 = 4, 5, 3
a = np.random.rand(d1, d2, d3)
print a

Suppose we want to extract all elements with index 1 in the last axis. This can be done easily using an ellipsis object as follows:

print a[...,1]

The preceding command line is equivalent to the following one:

print a[:,:,1]

It is also possible to augment the matrix along an axis when slicing, as follows:

print a[0, :, np.newaxis, 0]

Compare the output of the preceding command line with the output of the following:

print a[0, :, 0]