The `add()` function performs element-wise addition of two arrays.

### Example

``````import numpy as np

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

# perform element-wise addition of the two arrays

print(result)

# Output: [5 7 9]``````

The syntax of `add()` is:

``numpy.add(x1, x2, out = None, where = True, dtype = None)``

The `add()` function takes following arguments:

• `x1` and `x2` - two input arrays or scalars to be added
• `out` (optional) - the output array where the result will be stored
• `where` (optional) - a boolean array or condition specifying which elements to add
• `dtype` (optional) - data type of the output array

The `add()` function returns the array containing the sum of corresponding element(s) from two arrays — x1 and x2.

## Example 1: Add NumPy Array by scalar (Single Value)

``````import numpy as np

# create an array
array1 = np.array([1, 2, 3])

# add a scalar value to the array

print(result)``````

Output

`[11 12 13]`

Here, the `np.add()` function is used to add a scalar value of 10 to each element of the array1 array.

## Example 2: Use of out and where in add()

``````import numpy as np

# create two input arrays
array1 = np.array([1, 2, 3, 5])
array2 = np.array([10, 20, 30, 50])

# create a boolean array to specify the condition for element selection
condition = np.array([True, False, True, True])

# create an empty array to store the subtracted values
result = np.empty_like(array1)

# add elements in array1 and array2 based on values in the condition array and
# store the sum in the result array

print(result)``````

Output

`[11  0 33 55]`

The output shows the result of the addition operation, where the elements from array1 and array2 are added together only when the corresponding condition in the condition array is `True`.

The second element in result is 0 because the corresponding condition value is False, and therefore, the addition does not take place for that element.

Here, `out=result` specifies that the output of `np.add()` should be stored in the result array

## Example 3: Use of dtype Argument in add()

``````import numpy as np

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

# perform addition with floating-point data type

# perform addition with integer data type

# print the result with floating-point data type
print("Floating-point result:")
print(resultFloat)

# print the result with integer data type
print("Integer result:")
print(resultInt)``````

Output

```Floating-point result:
[5. 7. 9.]
Integer result:
[5 7 9]```

Here, by specifying the desired `dtype`, we can control the data type of the output array according to our requirements.

Here, we have specified the data type of the output array with the `dtype` argument.

Note: To learn more about the `dtype` argument, please visit NumPy Data Types.