Fancy Indexing
Summary: in this tutorial, you’ll learn about the fancy indexing technique to select elements of a numpy array.
Introduction to fancy indexing
In the previous tutorial, you learned how to select elements from a numpy array using indexing and slicing techniques.
Besides using indexing & slicing, NumPy provides you with a convenient way to index an array called fancy indexing.
Fancy indexing allows you to index a numpy array using the following:
- Another numpy array
- A Python list
- A sequence of integers
Let’s see the following example:
import numpy as npa = np.arange(1, 10)
print(a)
indices = np.array([2, 3, 4])
print(a[indices])
Code language: PHP (php)
Output:
[1 2 3 4 5 6 7 8 9]
[3 4 5]Code language: JSON / JSON with Comments (json)
How it works.
First, use the arange() function to create a numpy array that includes numbers from 1 to 9:
[1 2 3 4 5 6 7 8 9]Code language: JSON / JSON with Comments (json)
Second, create a second numpy array for indexing:
indices = np.array([2, 3, 4])Code language: PHP (php)
Third, use the indices array for indexing the a array:
print(a[indices])Code language: CSS (css)
Summary
- Fancy indexing allows you to index an array using another array, a list, or a sequence of integers.