NumPy add() – Add Two Array Element-wise

NumPy add()

The numpy.add() function performs element-wise addition of two input arrays. If the arrays have different shapes, they must be broadcastable to a common shape.

Syntax

numpy.add(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True)

Parameters

Parameter	Type	Description
x1, x2	array_like	Input arrays to be added. They must have the same shape or be broadcastable.
out	ndarray, None, or tuple of ndarray and None, optional	Optional output array where the result is stored. If None, a new array is created.
where	array_like, optional	Boolean mask specifying which elements to compute. Elements where where=False retain their original value.
casting	str, optional	Defines the casting behavior when computing the addition.
order	str, optional	Memory layout order of the output array.
dtype	data-type, optional	Defines the data type of the output array.
subok	bool, optional	Determines if subclasses of ndarray are preserved in the output.

Return Value

Returns an array with the element-wise sum of x1 and x2. If both inputs are scalars, a scalar is returned.

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Examples

1. Adding Two Scalars

Adding two scalar values using numpy.add().

import numpy as np

# Define two scalar values
x1 = 10
x2 = 5

# Perform element-wise addition
result = np.add(x1, x2)

# Print the result
print("Sum of scalars:", result)

Output:

Sum of scalars: 15

2. Adding Two Arrays Element-Wise

Adding two arrays of the same shape element-wise.

import numpy as np

# Define two arrays
x1 = np.array([1, 2, 3, 4])
x2 = np.array([5, 6, 7, 8])

# Perform element-wise addition
result = np.add(x1, x2)

# Print the results
print("Array 1:", x1)
print("Array 2:", x2)
print("Sum of arrays:", result)

Output:

Array 1: [1 2 3 4]
Array 2: [5 6 7 8]
Sum of arrays: [ 6  8 10 12]

3. Adding Arrays with Broadcasting

Adding a 1D array to a 2D array using broadcasting.

import numpy as np

# Define a 2D array and a 1D array
x1 = np.array([[1, 2, 3], [4, 5, 6]])
x2 = np.array([10, 20, 30])  # Broadcasted to match x1

# Perform element-wise addition
result = np.add(x1, x2)

# Print the results
print("2D Array:\n", x1)
print("1D Array:", x2)
print("Sum with broadcasting:\n", result)

Output:

2D Array:
 [[1 2 3]
 [4 5 6]]
1D Array: [10 20 30]
Sum with broadcasting:
 [[11 22 33]
 [14 25 36]]

4. Using the out Parameter

Storing the addition result in an existing array.

import numpy as np

# Define two arrays
x1 = np.array([1, 2, 3, 4])
x2 = np.array([10, 20, 30, 40])

# Create an output array with the same shape
output_array = np.empty_like(x1)

# Perform addition and store in output_array
np.add(x1, x2, out=output_array)

# Print the result
print("Output array after addition:", output_array)

Output:

Output array after addition: [11 22 33 44]

5. Using the where Parameter

Performing addition only at selected positions using a boolean mask.

import numpy as np

# Define two arrays
x1 = np.array([1, 2, 3, 4])
x2 = np.array([10, 20, 30, 40])

# Define a mask (perform addition only where mask is True)
mask = np.array([True, False, True, False])

# Perform addition with condition
result = np.add(x1, x2, where=mask)

# Print the result
print("Result with where condition:", result)

Output:

Result with where condition: [11  2 33  4]

Elements where mask=False retain their original values from x1.