Select elements using Mask Indexing in 2D NumPy arrays

NumPy: Advanced Indexing Exercise-20 with Solution

Indexing with Masks:

Write a NumPy program that creates a 2D NumPy array and uses a mask array (boolean array) for indexing to select a subset of elements that match the mask criteria.

Sample Solution:

Python Code:

import numpy as np

# Create a 2D NumPy array of shape (5, 5) with random integers
array_2d = np.random.randint(0, 100, size=(5, 5))

# Define a mask array to select elements that are greater than 50
mask = array_2d > 50

# Use the mask array for indexing to select elements that match the mask criteria
selected_elements = array_2d[mask]

# Print the original array, the mask array, and the selected elements
print('Original 2D array:\n', array_2d)
print('Mask array (elements > 50):\n', mask)
print('Selected elements using the mask:\n', selected_elements)

Output:

Original 2D array:
 [[83 21 74 71  3]
 [55  2 86 46 33]
 [58 37 41 29 33]
 [45 79 99 81 87]
 [38 80 47 68 45]]
Mask array (elements > 50):
 [[ True False  True  True False]
 [ True False  True False False]
 [ True False False False False]
 [False  True  True  True  True]
 [False  True False  True False]]
Selected elements using the mask:
 [83 74 71 55 86 58 79 99 81 87 80 68]

Explanation:

• Import Libraries:
• Imported numpy as "np" for array creation and manipulation.
• Create 2D NumPy Array:
• Create a 2D NumPy array named array_2d with random integers ranging from 0 to 99 and a shape of (5, 5).
• Define Mask Array:
• Define a mask array to select elements in the array that are greater than 50.
• Indexing with Mask:
• Used the mask array for indexing to select elements from array_2d that match the mask criteria.
• Print Results:
• Print the original 2D array, the mask array, and the selected elements to verify the indexing operation

Python-Numpy Code Editor:

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