# 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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