Python:NumPy ndim

The ndim attribute returns the total number of dimensions (axes) of a NumPy array. A 1D array acts like a list, a 2D array forms a matrix, and higher dimensions represent tensors which are common in data science and machine learning.

Syntax

ndarray.ndim

Parameters:

The ndim attribute takes no parameters.

Return value:

Returns an integer representing the number of array dimensions (axes).

Example: Checking Dimensions of Different Arrays

This example demonstrates how ndim behaves for 0D (scalar), 1D (list), and 2D (matrix) arrays:

import numpy as np

# 0D array (scalar)
arr_0d = np.array(50)
print("0D array:", arr_0d, "| Dimensions:", arr_0d.ndim)

# 1D array (list)
arr_1d = np.array([1, 2, 3, 4, 5])
print("1D array:", arr_1d, "| Dimensions:", arr_1d.ndim)

# 2D array (matrix)
arr_2d = np.array([[1, 2, 3], [4, 5, 6]])
print("2D array:\n", arr_2d, "\nDimensions:", arr_2d.ndim)

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The output of this code is:

0D array: 50 | Dimensions: 0
1D array: [1 2 3 4 5] | Dimensions: 1
2D array:
 [[1 2 3]
 [4 5 6]]
Dimensions: 2

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Codebyte Example: Using ndim in a NumPy Operation

In this example, ndim is used to identify the number of dimensions in different types of image data like grayscale (2D) and color (3D):

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