[Term Entry] Python NumPy - ndarray: ndim

* add .ndim docs to python numpy array (#7821)

* add a new term entry for NumPy- ndarray.ndim(#7821)

* Update ndim.md examples

* Update ndim.md

* Minor changes

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Author: VictorTeppeh233

content/numpy/concepts/ndarray/terms/ndim/ndim.md

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+---
+Title: 'ndim'
+Description: 'Returns the number of dimensions (axes) of a NumPy array.'
+Subjects:
+  - 'Code Foundations'
+  - 'Computer Science'
+Tags:
+  - 'Arrays'
+  - 'Attributes'
+  - 'NumPy'
+  - 'Shape'
+CatalogContent:
+  - 'learn-python-3'
+  - 'paths/computer-science'
+---
+
+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
+
+```pseudo
+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:
+
+```py
+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)
+```
+
+The output of this code is:
+
+```shell
+0D array: 50 | Dimensions: 0
+1D array: [1 2 3 4 5] | Dimensions: 1
+2D array:
+ [[1 2 3]
+ [4 5 6]]
+Dimensions: 2
+```
+
+## 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):
+
+```codebyte/python
+import numpy as np
+
+arr_a = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
+
+# Print the array and its number of dimensions
+print(f"array: {arr_a}, number of dimensions: {arr_a.ndim}")
+```