The numpy add function computes the sum of the two arrays. It computes the addition of two arrays, say l1 and l2, element by element. The numpy.add() function is a universal function, which means that it accepts several parameters that allow you to optimize its work based on the algorithm’s specifics.

Syntax:

numpy.add(l1, l2, out=None)

Parameters:

l1 and l2: (Required)

Required. The arrays that will be added must be specified. They must be broadcastable to a common shape if l1.shape!= l2.shape

out:

This is optional. It is the location where the result will be saved. It must have a shape that the inputs broadcast to if it is provided. If None or not given, a newly allocated array is returned.

Return Value:

The addition between l1 and l2 is returned by the add function. The nd-add() array’s method can be a scalar. It is determined by the l1 and l2 variables. Numpy is used if l1 and l2 are both scalar. A scalar value will be returned by add(). Otherwise, an nd-array will be returned.

## NumPy add() Function in Python

Approach:

• Import NumPy module using the import keyword.
• Pass some random list as an argument to the array() function to create an array.
• Store it in a variable.
• Create some sample arrays of different shapes to test the add() function.
• Pass the first array and some random number to add() function of NumPy module and print the result.
• Here it adds the second argument for each element of the array.
• Print the result array.
• Pass the first array and second array to add() function of NumPy module and print the result.
• Here it adds the second array elements for the first array element.
• Print the result array.
• Similarly, test it with other arrays of different shapes.
• The Exit of the Program.

Below is the implementation:

# Import NumPy module using the import keyword.
import numpy as np
# Pass some random list as an argument to the array() function to create an array.
# Store it in a variable.
arry1 = np.array([[30, 60], [90, 120], [150, 180]])
# Create some sample arrays of different shapes to test the add() function.
arry2 = np.array([[10, 20]])
arry3 = np.array([[100], [200], [300]])
arry4 = np.array([[55, 65], [75, 85], [95, 105]])
# Pass the first array and some random number to add() function of NumPy module and print the result.
# Here it adds the second argument for each element of the array.
print('Adding random number say 10 to the first array : ')
# Print the result array.
# Pass the first array and second array to add() function of NumPy module and print the result.
# Here it adds the second array elements for the first array element.
# Print the result array.
print('Adding first array and second array : ')
# Similarly, test it with other arrays of different shapes.
print('Adding first array and third array : ')
print('Adding first array and fourth array : ')


Output:

Adding random number say 3 to the first array :
Adding 10 to first array gives :
[[ 40 70]
[100 130]
[160 190]]
Adding first array and second array :
[[ 40 80]
[100 140]
[160 200]]
Adding first array and third array :
[[130 160]
[290 320]
[450 480]]
Adding first array and fourth array :
[[ 85 125]
[165 205]
[245 285]]