Understanding Fancy Indexing
Fancy indexing uses arrays of indices to select elements from a NumPy array. This means you can select multiple, non-contiguous elements simultaneously. This contrasts with basic indexing which uses a single integer or a slice to access a contiguous sequence of elements.
Let’s illustrate with a simple example:
import numpy as np
arr = np.array([10, 20, 30, 40, 50])
print(arr[0]) # Output: 10
print(arr[1:3]) # Output: [20 30]
indices = np.array([0, 2, 4])
print(arr[indices]) # Output: [10 30 50]
indices = np.array([1, 3, 0])
print(arr[indices]) # Output: [20 40 10]As you can see, fancy indexing allows us to select elements in any order, regardless of their position in the array. This flexibility is crucial for many data manipulation tasks.
Multi-Dimensional Fancy Indexing
The true power of fancy indexing shines when working with multi-dimensional arrays. You can use multiple arrays of indices to select elements along different axes.
arr_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
row_indices = np.array([0, 2])
col_indices = np.array([1, 2])
print(arr_2d[row_indices, col_indices]) # Output: [2 9]This example selects elements at (0,1), (2,2) simultaneously.
Boolean Indexing: A Special Case of Fancy Indexing
Boolean indexing is a powerful variation of fancy indexing where you use a boolean array to select elements. Elements corresponding to True values in the boolean array are selected.
arr = np.array([1, 2, 3, 4, 5, 6])
bool_arr = np.array([True, False, True, False, True, False])
print(arr[bool_arr]) # Output: [1 3 5]
#Even better: Create the boolean array directly using a condition
print(arr[arr > 3]) # Output: [4 5 6]Boolean indexing is extremely useful for filtering data based on certain conditions.
Advanced Techniques: Combining Indexing Methods
You can combine different indexing methods for more complex selections. For instance, you can use a combination of integer indexing, slicing, and fancy indexing.
arr_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(arr_2d[1:, [0,2]]) # Output: [[4 6] [7 9]]This selects rows from index 1 onwards and columns 0 and 2.
Modifying Arrays with Fancy Indexing
Fancy indexing isn’t just for selecting elements; it’s also effective for modifying them.
arr = np.array([1, 2, 3, 4, 5])
indices = np.array([0, 2, 4])
arr[indices] = 100
print(arr) #Output: [100 2 100 4 100]This example demonstrates how easily you can modify specific elements using fancy indexing. The flexibility and power of fancy indexing make it an indispensable tool for any serious NumPy user.