Select elements from 3D NumPy array using integer Indexing

Last update on December 21 2024 07:40:16 (UTC/GMT +8 hours)

Integer Array Indexing on 3D Arrays:

Write a NumPy program that creates a 3D NumPy array and uses integer array indexing to select elements along specific axes.

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 indices to select specific elements along each axis
depth_indices = np.array([0, 1, 2])
row_indices = np.array([1, 2, 3])
column_indices = np.array([2, 3, 4])

# Use integer array indexing to select elements along specific axes
selected_elements = array_3d[depth_indices[:, np.newaxis, np.newaxis], row_indices[:, np.newaxis], column_indices]

# Print the original array and the selected elements
print('Original 3D array:\n', array_3d)
print('Depth indices:\n', depth_indices)
print('Row indices:\n', row_indices)
print('Column indices:\n', column_indices)
print('Selected elements:\n', selected_elements)

Output:

 Original 3D array:
 [[[ 5 97 52 61 57]
  [79 87 75 83 21]
  [52  1 33 54 10]
  [76 58 44  0 72]]

 [[40  7 30 18 61]
  [24  1  9 98 25]
  [77 75  3 82  5]
  [90 63 59 79 52]]

 [[49 69 60 80 28]
  [45 60 63 31 69]
  [18 49 62 25 87]
  [85 94 35  9  8]]]
Depth indices:
 [0 1 2]
Row indices:
 [1 2 3]
Column indices:
 [2 3 4]
Selected elements:
 [[[75 83 21]
  [33 54 10]
  [44  0 72]]

 [[ 9 98 25]
  [ 3 82  5]
  [59 79 52]]

 [[63 31 69]
  [62 25 87]
  [35  9  8]]]

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 Indices:
• Defined depth_indices, row_indices, and column_indices arrays to specify the indices along the depth, row, and column axes, respectively.
• Integer Array Indexing:
• Used integer array indexing to select elements from 'array_3d' based on the specified depth, row, and column indices.
• Print Results:
• Print the original 3D array, the depth, row, and column indices, and the selected elements to verify the indexing operation.

Python-Numpy Code Editor: