# How do you multiply each element by a matrix in NumPy?

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

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]]
0
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]]
1

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]]
2
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]]
0
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]]
4

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]]
6
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]]
7
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]]
8
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]]
9

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]]
6
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]]
7
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]]
2
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]]
3

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]]

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]]
5
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]]
0
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]]
7

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]]
6
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]]
7numpy.multiply()0numpy.multiply()1

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

numpy.multiply()8

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]]
0 # Python program explaining0# Python program explaining1# Python program explaining2# Python program explaining3# Python program explaining4# Python program explaining2# Python program explaining6# Python program explaining7# Python program explaining3
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]]
4# Python program explaining2# Python program explaining1# Python program explaining2# numpy.multiply() function3# numpy.multiply() function4

# numpy.multiply() function5

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]]
0 # Python program explaining0# numpy.multiply() function3# Python program explaining2# Python program explaining3# Python program explaining4# Python program explaining2 3# Python program explaining7# Python program explaining6# Python program explaining2# Python program explaining3# Python program explaining1# Python program explaining2import0# numpy.multiply() function4

import

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]]
6
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]]
7import5import6

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]]
6
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]]
7import9numpy as geek0

import

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]]

numpy as geek3

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]]
0 numpy as geek5

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]]
6
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]]
7numpy as geek8numpy as geek9

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

numpy.multiply()8

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]]
0in_num14# Python program explaining1# Python program explaining2# Python program explaining3# Python program explaining4# Python program explaining2# Python program explaining6# Python program explaining7# Python program explaining3
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]]
4# Python program explaining2# Python program explaining1# Python program explaining2# numpy.multiply() function3# numpy.multiply() function4

# numpy.multiply() function5

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]]
0 in_num14# numpy.multiply() function3# Python program explaining2# Python program explaining3# Python program explaining4# Python program explaining2 3# Python program explaining7# Python program explaining6# Python program explaining2# Python program explaining3# Python program explaining1# Python program explaining2import0# numpy.multiply() function4

### How do you multiply matrices by element in NumPy?

There are three main ways to perform NumPy matrix multiplication:.
dot(array a, array b) : returns the scalar or dot product of two arrays..
matmul(array a, array b) : returns the matrix product of two arrays..
multiply(array a, array b) : returns the element-wise matrix multiplication of two arrays..

### How do you multiply each matrix element?

Matrix Multiplication.
When you multiply a matrix by a number, you multiply every element in the matrix by the same number. ... .
For example, if x is 5, and the matrix A is:.
Then, xA = 5A and..
In the example above, every element of A is multiplied by 5 to produce the scalar multiple, B..

### How do you multiply each element of a matrix by a number in Python?

You can use simple * to to element-wise multiplication with numpy arrays. This is a good clear answer.