# NumPy multiply()

The `multiply()` function is performs element-wise multiplication of two arrays.

``````import numpy as np

array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])

# perform element-wise multiplication between array1 and array2
result = np.multiply(array1, array2)

print(result)

# Output : [ 4 10 18]``````

## multiply() Syntax

The syntax of `multiply()` is:

``numpy.multiply(array1, array2, out=None)``

## multiply() Arguments

The `multiply()` function takes following arguments:

• `array1` and `array2` - two input arrays to be multiplied element-wise
• `out` (optional) - the output array where the result will be stored

Note: `array1` and `array2` must have the same shape unless one of them is a scalar value.

## multiply() Return Value

The `multiply()` function returns an array that contains the result of element-wise multiplication between the input arrays.

## Example 1: Multiply Two Arrays

``````import numpy as np

array1 = np.array([10, 20, 30])
array2 = np.array([2, 4, 6])

# perform element-wise multiplication between arrays array1 and array2
result = np.multiply(array1, array2)

print(result)``````

Output

`[ 20  80 180]`

## Example 2: Multiplication of an Array by a Scalar

``````import numpy as np

array1 = np.array([1, 2, 3])
scalar = 2

# multiply each element in array1 by the scalar value
result = np.multiply(array1, scalar)

print(result)``````

Output

`[2 4 6]`

In this example, we multiplied each element in array1 by the scalar value of 2.

## Example 3: Use out to Store Result in a Desired Array

``````import numpy as np

array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])

# create an empty array with the same shape as array1 and array2
result = np.zeros_like(array1)

# perform element-wise multiplication of array1 and array2 and store the result in result
np.multiply(array1, array2, out=result)

print(result)``````

Output

`[ 4 10 18]`

Here, after specifying `out=result`, the result of element-wise multiplication of array1 and array2 is stored in the result array.