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ICSM Computer

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.).

Syntax

array.shape

Example 1: 1D Array

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.

Example 2: 2D Array

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

Output:

(2, 3)

This means there are 2 rows and 3 columns.

Example 3: 3D Array

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.

Example 4: Using .reshape() to change shape

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)

Example 5: Get number of dimensions

print(arr.ndim)

Output:

1

Returns the number of dimensions.

Summary

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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