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Boolean Indexing on 3D NumPy arrays with conditions

Boolean Indexing on Multi-dimensional Arrays:

Write a NumPy program that creates a 3D NumPy array and use boolean indexing to select elements along one axis based on conditions applied to another axis.

Sample Solution:

Python Code:

import numpy as np

# Create a 3D NumPy array of shape (3, 4, 5) with random integers
array_3d = np.random.randint(0, 100, size=(3, 4, 5))

# Define the condition to apply on the second axis (axis 1)
condition = array_3d[:, :, 0] > 50

# Use boolean indexing to select elements along the third axis (axis 2)
selected_elements = array_3d[condition]

# Print the original 3D array and the selected elements
print('Original 3D array:\n', array_3d)
print('Condition array (elements along second axis where first element > 50):\n', condition)
print('Selected elements along third axis based on condition:\n', selected_elements)

Output:

Original 3D array:
 [[[53  8 69 33 11]
  [54 91  9 13 78]
  [92 26  6 24 84]
  [19 21 20 74 24]]

 [[65 46 48 57 31]
  [12 62 64 73 68]
  [55 61 61 62 31]
  [16 34 35 64 25]]

 [[24 85 19 60 27]
  [25 26 59 78 81]
  [89 77 22 29 60]
  [ 9 32 45 62 25]]]
Condition array (elements along second axis where first element > 50):
 [[ True  True  True False]
 [ True False  True False]
 [False False  True False]]
Selected elements along third axis based on condition:
 [[53  8 69 33 11]
 [54 91  9 13 78]
 [92 26  6 24 84]
 [65 46 48 57 31]
 [55 61 61 62 31]
 [89 77 22 29 60]]

Explanation:

  • Import Libraries:
    • Imported numpy as 'np' for array creation and manipulation.
  • Create 3D NumPy Array:
    • Create a 3D NumPy array named array_3d with random integers ranging from 0 to 99 and a shape of (3, 4, 5).
  • Define Condition:
    • Define a condition to apply on the second axis (axis 1) by checking if the first element along the third axis (axis 2) is greater than 50.
  • Boolean Indexing:
    • Applied boolean indexing to select elements along the third axis (axis 2) based on the condition applied to the second axis (axis 1).
  • Print Results:
    • Print the original 3D array, the condition array, and the selected elements to verify the indexing operation.

Python-Numpy Code Editor: