NumPy `add()` is a mathematical function and is used to calculate the addition between two NumPy arrays. This function adds given arrays element-wise. The add() function returns a scalar or nd-array. If shapes of two arrays are not same, that is `arr.shape!=arr1.shape`, they must be broadcastable to a common shape. In this article, I will explain how to use the NumPy add() function with examples.

## 1. Quick Examples of Array Addition.

If you are in a hurry, below are some quick examples of NumPy array addition.

``````
# Below are some quick examples

arr = [2, 4, 6, 8, 12, 14]

arr = 12
arr1 = 25

# Example 3: Use numpy add() function with two input arrays
arr = np.array([2, 6, 9])
arr1 = np.array([5, 8, 12])

# Example 4: Use numpy.ndarray.add() function

# Example 5: Use + operator
arr2 = arr + arr1

# Example 6: Add numpy two multi-dimensional arrays
arr = [[18, 25, 37], [5, -7, 15]]
arr1 = [[4, 8, 12], [-13, 24, 17]]
``````

## 2. Syntax of NumPy add() Function

Following is the syntax of the numpy.add() function.

``````
numpy.add(arr, arr1, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) =
``````

Following are the parameters of the `add()` function.

• `arr `– The first input array or object works as an addition.
• `arr1` – Second input array or object which works as an addition.
• `out `– It is ndarray, None, or tuple of ndarray and None, optional. Out will be the location where the result is to be stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple must have a length equal to the number of outputs.
• `where` – This condition is broadcast over the input. True value means to calculate the unfunc at that position, whereas the False indicates to leave the value in the output alone.
• `**kwargs` – Allows passing keyword variable length of argument to a function. Used when we want to handle a named argument in a function.
• `order` – {‘C’, ‘F’, ‘A’, ‘K’}, optional: ‘C’: means to flatten in row-major using C-style order. ‘F’: means to flatten in column-major (Fortran- style) order. ‘A’: means to flatten in column-major order if a is Fortran contiguous in memory, row-major order otherwise. K’: means to flatten an in the order the elements occur in memory. By default, the ‘C’ index order is used.
• `dtype` – It is an optional parameter that specifies the data type of the returned array.

### 2.2 Return Value of NumPy add()

It returns nd-array. If both arr and arr1 are scalars then it returns a scalar.

## 3. Usage of NumPy add() Function

The` numpy.add()` is a mathematical function and is used to calculate the addition between two NumPy arrays. It will return the nd-array.

## 4. Add NumPy Array by scalar (Single Value)

We can add the array with a scalar value for that, we have to take an array named `arr` and the scalar value is `12` then we will pass the array and scalar value as an argument in `numpy.add()` function. It will return the nd-array.

``````
import numpy as np

# Creating an 1D input array
arr = [2, 4, 6, 8, 12, 14]

print(arr2)

# Output:
# [14 16 18 20 24 26]
``````

If either `arr `or `arr1` is 0-D(scalar) then `numpy.add(arr,arr1)` is equivalent to adding two numbers (a +b).

``````
# Creating two numbers
arr = 12
arr1 = 25

print(arr2)

# Output:
# 37
``````

## 6. Use NumPy add() Function with Two Input Arrays

Add our two NumPy arrays using `numpy.add()` function. It returns the addition of two arrays.

``````
# Create NumPy 2-D array
arr = np.array([2, 6, 9])
arr1 = np.array([5, 8, 12])

# Use numpy add() function with two input arrays
print(arr2)

# Output:
# [ 7 14 21]
``````

We can use `numpy.ndarray.add()` function is used to add some value to every element of the array. We can use it to perform vector addition by passing the second array to this function.

``````
print(arr2)
``````

Yields the same output as above.

## 8. Use + Operator

We can also use the arithmetic` +` operator to calculate the sum of two arrays. The result is same as the above.

``````
# Use + operator
arr2 = arr + arr1
print(arr2)
``````

## 9. Add NumPy Two Multi-Dimensional Arrays

Using the `add()` function we can add the two multi-dimensional arrays elementwise. The resultant array is the same shape as the input arrays.

``````
# Creating two multi-dimensional input array
arr = [[18, 25, 37], [5, -7, 15]]
arr1 = [[4, 8, 12], [-13, 24, 17]]

# Add numpy two multi-dimensional arrays
print(arr2)

# Output:
# [[22 33 49]
#  [-8 17 32]]
``````

## 10. Conclusion

In this article, I have explained how to use `numpy.add()` function and using how to calculate the addition of NumPy array.

Happy Learning!!

## References

### Vijetha

With 5 of experience in technical writing, I have had the privilege to work with a diverse range of technologies like Python, Pandas, NumPy and R. During this time, I have consistently demonstrated my ability to grasp intricate technical details and transform them into comprehensible materials. 