Boolean indexing, Fancy indexing and sub setting of an array | S-Logix

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How to Do Boolean Indexing, Fancy Indexing and Subsetting of an Array Using NumPy

NumPy Indexing

Condition for Boolean Indexing, Fancy Indexing, and Subsetting

Description:
In NumPy, Boolean indexing, fancy indexing, and subsetting are essential techniques to manipulate data in arrays:

Boolean Indexing: Filters elements of an array based on a condition (True/False mask).
Fancy Indexing: Uses an array of indices to access specific elements of an array.
Subsetting: Refers to selecting specific parts of an array using slicing or advanced indexing techniques.

Step-by-Step Process

Import NumPy:
Import the NumPy library using import numpy as np.
Create a NumPy Array:
Create an array using np.array().
Boolean Indexing:
Apply a condition on the array to filter elements, such as arr > 5.
Fancy Indexing:
Select elements using an array of indices or multi-dimensional indexing.
Subsetting:
Use slicing (:) to extract specific rows, columns, or subarrays from the array.

Sample Source Code

# Code for Boolean Indexing, Fancy Indexing, and Subsetting

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

# Boolean Indexing
boolean_mask = arr > 5
print("Boolean Indexing (Elements greater than 5):")
print(arr[boolean_mask])

# Fancy Indexing
indices = [1, 3, 5, 7]
fancy_result = arr[indices]
print("\nFancy Indexing (Elements at indices 1, 3, 5, 7):")
print(fancy_result)

# Subsetting (Slicing)
subset_result = arr[2:6]
print("\nSubsetting (Elements from index 2 to 6):")
print(subset_result)

Screenshots

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