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numpy.multiply() in Python - GeeksforGeeks

Last Updated : 11 Jul, 2025

The numpy.multiply() is a numpy function in Python which is used to find element-wise multiplication of two arrays or scalar (single value). It returns the product of two input array element by element.

Syntax:

numpy.multiply(arr1, arr2, out=None, where=True, casting='same_kind', order='K', dtype=None, subok=True)

Parameters:

• arr1 (array_like or scalar): First input array.
• arr2 (array_like or scalar): Second input array.
• dtype (optional): Desired type of the returned array. By default dtype of arr1 is used.
• out (optional, ndarray): A location where result is stored. If not provided a new array is created.
• where (optional, array_like): A condition to find where multiplication should happen. If True multiplication occurs at that position and if False value in output remains unchanged.

Return: ndarray (Element-wise product of arr1 and arr2).

• out_num = geek.multiply(in_num1, in_num2): Multiplies in_num1andin_num2 using multiply and stores the result in out_num.

import numpy as geek
in_num1 = 4
in_num2 = 6
print ("1st Input  number : ", in_num1)
print ("2nd Input  number : ", in_num2)
out_num = geek.multiply(in_num1, in_num2) 
print ("output number : ", out_num) 

Output:

When one of the inputs is a scalar it is multiplied with each element of the array. This operation is commonly used for scaling or adjusting values in an array. Here Scalar value 5 is multiplied with each element 1,2,3.

import numpy as geek
in_arr = geek.array([1, 2, 3])
scalar_value = 5

result_arr = geek.multiply(in_arr, scalar_value)
print(result_arr)

Output:

[ 5 10 15]

When both inputs are arrays of the same shape numpy.multiply() multiplies corresponding elements together. This operation is performed element by element.

import numpy as geek
in_arr1 = geek.array([[2, -7, 5], [-6, 2, 0]])
in_arr2 = geek.array([[0, -7, 8], [5, -2, 9]])
print ("1st Input array : ", in_arr1)
print ("2nd Input array : ", in_arr2)
out_arr = geek.multiply(in_arr1, in_arr2) 
print ("Resultant output array: ", out_arr) 

Output

In above example corresponding elements from in_arr1 and in_arr2 are multiplied:

• 2 * 0 = 0
• -7 * -7 = 49
• 5 * 8 = 40
• -6 * 5 = -30
• 2 * -2 = -4
• 0 * 9 = 0

numpy.multiply() supports broadcasting which means it can multiply arrays with different shapes as long as they are compatible for broadcasting rules.

import numpy as geek
in_arr1 = geek.array([1, 2, 3])
in_arr2 = geek.array([[4], [5], [6]])

result_arr = geek.multiply(in_arr1, in_arr2)
print(result_arr)

Output:

In this example in_arr1 is broadcasted to match the shape of in_arr2 for element-wise multiplication.

To understand broadcasting you can refer to this article: NumPy Array Broadcasting

We can specify an output array where result of the multiplication will be stored. This avoids creating a new array and can help save memory when working with large datasets.

import numpy as geek
in_arr1 = geek.array([1, 2, 3])
in_arr2 = geek.array([4, 5, 6])
output_arr = geek.empty_like(in_arr1)
geek.multiply(in_arr1, in_arr2, out=output_arr)
print(output_arr)

Output:

[ 4 10 18]

Result of element-wise multiplication is stored in output_arr instead of creating a new array. By mastering numpy.multiply() we can efficiently handle element-wise multiplication across arrays and scalars.

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