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An array with dimensions (3, 3, 2) represents a 3-dimensional array. Each dimension describes the structure and organization of the data within the array.
Explanation of Dimensions
First Dimension (3): This indicates that the array contains 3 "blocks" or "layers." Each block is essentially a 2D array.
Second Dimension (3): Each block contains 3 rows.
Third Dimension (2): Each row contains 2 elements.
In simpler terms, this array can be visualized as 3 layers, where each layer is a 3x2 matrix.
Example Representation
Here’s how a (3, 3, 2) array might look:
import numpy as np# Creating a 3D array with dimensions (3, 3, 2)array = np.array([[[1, 2], [3, 4], [5, 6]], # First layer[[7, 8], [9, 10], [11, 12]], # Second layer[[13, 14], [15, 16], [17, 18]] # Third layer])print(array.shape) # Output: (3, 3, 2)Copied!âś•CopyBreakdown of the Example
The array has 3 layers.
Each layer has 3 rows.
Each row contains 2 elements.
Total Elements
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