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Explain the NumPy Array Shape

ICSM Computer 488 10 Nov 2025

ICSM Computer 10 Nov 2025

In NumPy, the shape of an array tells you how many elements it has along each dimension (rows, columns, etc.).

array.shape

import numpy as np

arr = np.array([10, 20, 30, 40])
print(arr.shape)

Output:

(4,)

This means the array has 4 elements in 1 dimension.

arr = np.array([[1, 2, 3],
                [4, 5, 6]])
print(arr.shape)

Output:

(2, 3)

This means there are 2 rows and 3 columns.

arr = np.array([
    [[1, 2], [3, 4]],
    [[5, 6], [7, 8]]
])
print(arr.shape)

Output:

(2, 2, 2)

This means there are 2 blocks, each containing a 2×2 matrix.

arr = np.array([1, 2, 3, 4, 5, 6])
new_arr = arr.reshape(2, 3)
print(new_arr)
print(new_arr.shape)

Output:

[[1 2 3]
 [4 5 6]]
(2, 3)

print(arr.ndim)

Output:

Returns the number of dimensions.

Property	Meaning	Example Result
`arr.shape`	Returns shape (rows, cols, etc.)	`(3, 4)`
`arr.ndim`	Returns number of dimensions	`2`
`arr.size`	Returns total number of elements	`12`

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