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numpy.multiply() in Python

  • Difficulty Level : Easy
  • Last Updated : 16 May, 2020

numpy.multiply() function is used when we want to compute the multiplication of two array. It returns the product of arr1 and arr2, element-wise.

Syntax : numpy.multiply(arr1, arr2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature, extobj], ufunc ‘multiply’)

Parameters :
arr1: [array_like or scalar]1st Input array.
arr2: [array_like or scalar]2nd Input array.
dtype: The type of the returned array. By default, the dtype of arr is used.
out: [ndarray, optional] A location into which the result is stored.
  -> If provided, it must have a shape that the inputs broadcast to.
  -> If not provided or None, a freshly-allocated array is returned.
where: [array_like, optional] Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.
**kwargs: Allows to pass keyword variable length of argument to a function. Used when we want to handle named argument in a function.

Return: [ndarray or scalar] The product of arr1 and arr2, element-wise.

Example #1 :




# Python program explaining
# numpy.multiply() function
  
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 :
1st Input number :  4
2nd Input number :  6
output number :  24

 

Example #2 :
The following code is also known as the Hadamard product which is nothing but the element-wise-product of the two matrices. It is the most commonly used product for those who are interested in Machine Learning or statistics.




# Python program explaining
# numpy.multiply() function
  
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 :
1st Input array :  [[ 2 -7  5]
 [-6  2  0]]
2nd Input array :  [[ 0 -7  8]
 [ 5 -2  9]]
Resultant output array:  [[  0  49  40]
 [-30  -4   0]]

Another way to find the same is




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

Output :

1st Input array :  [[ 2 -7  5]
 [-6  2  0]]
2nd Input array :  [[ 0 -7  8]
 [ 5 -2  9]]
Resultant output array:  [[  0  49  40]
 [-30  -4   0]]

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