In a 3D array, elements are organized in three dimensions, and their positions are specified using three indices, typically written as [i][j][k] or (i, j, k). Here’s a breakdown:

How Indexing Works:

This hierarchy lets you pinpoint any element in the 3D array.

How Indexing Works:

1. First Dimension (i):

• Determines which “block” or “layer” of the array you’re accessing.
• Think of it as choosing a specific 2D “sheet” within the 3D array.

1. Second Dimension (j):

• Specifies the “row” within the selected 2D sheet.

1. Third Dimension (k):

• Points to the “column” within the chosen row.

This hierarchy lets you pinpoint any element in the 3D array.

Example:

Imagine a 3D array as a stack of 2D grids:

Layer 0:
[[1, 2, 3],
 [4, 5, 6]]

Layer 1:
[[7, 8, 9],
 [10, 11, 12]]

• To access the number 9, you’d use indices [1][0][2]:
• 1: Select the second layer (index 1 because indexing starts at 0).
• 0: Within that layer, pick the first row.
• 2: Then, take the third element in that row.

In Code (Using NumPy):

import numpy as np

# Create a 3D array
array = np.array([[[1, 2, 3], [4, 5, 6]],
                  [[7, 8, 9], [10, 11, 12]]])

# Access element 9
element = array[1, 0, 2]
print(element)  # Output: 9

This structure makes it easy to navigate, slice, and manipulate data in three-dimensional space.