Combine slicing and Indexing in NumPy to select elements

Last update on December 21 2024 07:36:22 (UTC/GMT +8 hours)

NumPy: Advanced Indexing Exercise-18 with Solution

Combining Slicing and Indexing:

Write a Numpy program that creates a 3D NumPy array and use a combination of slicing and integer indexing to select a specific slice and then index into it.

Sample Solution:

Python Code:

import numpy as np

# Create a 3D NumPy array of shape (4, 5, 6) with random integers
array_3d = np.random.randint(0, 100, size=(4, 5, 6))

# Use slicing to select a specific slice along the first axis
slice_2d = array_3d[2, :, :]

# Define the row and column indices to further index into the 2D slice
row_indices = np.array([0, 2, 4])
col_indices = np.array([1, 3, 5])

# Use integer indexing to select specific elements from the 2D slice
selected_elements = slice_2d[row_indices, col_indices]

# Print the original array, the 2D slice, and the selected elements
print('Original 3D array:\n', array_3d)
print('2D slice (3rd slice along first axis):\n', slice_2d)
print('Row indices:', row_indices)
print('Column indices:', col_indices)
print('Selected elements from 2D slice:\n', selected_elements)

Output:

Original 3D array:
 [[[81 97 48  7 72 45]
  [50 71 41 71 60 88]
  [42 26 78 23 47 64]
  [49  5 94 62  4  5]
  [12 29 69 68 90  6]]

 [[15  1 54 84 50 94]
  [97 70 17 43 15 24]
  [30 83 91 86 36 54]
  [34 92  8 78 58 76]
  [13 51 98 44 64  9]]

 [[46 57 40 48 67 40]
  [33 68 34 31 69  5]
  [13 34 24 23 54 57]
  [67  0 51 22 84 21]
  [67 80 51 66 86  9]]

 [[33 39 45 19 52 83]
  [94 85 76 23 34 43]
  [85 40 29 89 46 26]
  [ 3 70 71 20 14 60]
  [47 71 86 36 90  2]]]
2D slice (3rd slice along first axis):
 [[46 57 40 48 67 40]
 [33 68 34 31 69  5]
 [13 34 24 23 54 57]
 [67  0 51 22 84 21]
 [67 80 51 66 86  9]]
Row indices: [0 2 4]
Column indices: [1 3 5]
Selected elements from 2D slice:
 [57 23  9]

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 (4, 5, 6).
• Select Specific Slice:
• Used slicing to select the 3rd slice along the first axis (array_3d[2, :, :]), resulting in a 2D slice.
• Define Indices:
• Defined row_indices and col_indices arrays to specify the rows and columns to be indexed within the 2D slice.
• Integer Indexing:
• Used integer indexing to select specific elements from the 2D slice based on the defined row and column indices.
• Print Results:
• Print the original 3D array, the 2D slice, and the selected elements to verify the indexing operation.

Python-Numpy Code Editor: