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• Post category:NumPy / Python

NumPy `add()` is a mathematical function and is used to calculate the addition between two NumPy arrays. You can perform array addition using the `+` operator or the `numpy.add()` function. The addition is performed element-wise, meaning each element in one array is added to the corresponding element in another array. The `add()` function returns a scalar or nd-array. If the shapes of two arrays are not the same, that is `arr.shape!=arr1.shape`, they must be broadcastable to a common shape.

In this article, I will explain the `numpy.add()` function, covering its syntax, parameters, return value, and how to use it for array addition with examples.

## 1. Quick Examples of Array Addition

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

``````
# Quick examples of array addition

# Example 1: Get addition values
arr = [2, 4, 6, 8, 12, 14]

# Example 2: Use numpy.add() function
arr = np.array(12)
arr1 = np.array(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 = np.array([[18, 25, 37], [5, -7, 15]])
arr1 = np.array([[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` (Optional) – A boolean array of the same shape as `arr` and `arr1`, where `True` indicates locations where the addition should be performed. The default is `True`.
• `**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` (Optional) – The data type of the output array. If not specified, the type is determined by the types of `arr` and `arr1`.

### 2.2 Return Value

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)

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

In the below example, first, you import the NumPy library as `np`. You create a 1D NumPy array `arr` with values `[2, 4, 6, 8, 12, 14]`. You use `np.add()` it to add the scalar value `12` to each element of the array. You print the array `arr2`, which contains the element-wise addition of `arr` and `12`. So, the final array `arr2` is `[14,16,18,20,24,26]`, where each element is the sum of the corresponding element in the original array `arr` and `12`.

``````
# Import numpy
import numpy as np

# Creating an 1D input array
arr = [2, 4, 6, 8, 12, 14]
print("Original array:\n",arr)

``````

Yields below output.

The `numpy.add()` function is designed for element-wise addition of arrays, but if you want to use it to add two individual numbers, you can create 0-dimensional NumPy arrays (scalars) and then use the function.

In the below example, `arr` and `arr1` are scalar NumPy arrays and `np.add()` is used to perform the addition operation element-wise, resulting in the sum of the two numbers. The `arr2` variable holds the final result, which is `37` in this case.

``````
# Creating two numbers
arr = np.array(12)
arr1 = np.array(25)

``````

Yields below output.

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

If you want to use the `numpy.add()` function with two input arrays for element-wise addition. For instance, you create two 1D NumPy arrays, `arr` and `arr1`. You use `np.add()` to add the corresponding elements of `arr` and `arr1`. The result is stored in `arr2`. You print the array `arr2`, which contains the element-wise addition of `arr` and `arr1`. So, the final array `arr2` is `[7, 14, 21]`, where each element is the sum of the corresponding elements in the original arrays `arr` and `arr1`.

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

# With two input arrays

# Output:
# First array: [2 6 9]
# Second array: [ 5  8 12]
# Result of element-wise addition: [ 7 14 21]
``````

You can use `numpy.ndarray.add()` function is used to add some value to every element of the array. You 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

You can use the `+` operator for element-wise addition of NumPy arrays. For example, the `+` operator is used to add the corresponding elements of `arr` and `arr1`, resulting in the array `[7, 14, 21]`. The `+` operator is overloaded for NumPy arrays, allowing for intuitive element-wise operations.

``````
# Use + operator
arr2 = arr + arr1
``````

Yields the same output as above.

## 9. Add NumPy Two Multi-Dimensional Arrays

When adding two multi-dimensional arrays in NumPy, the addition is performed element-wise. The arrays must have the same shape or be broadcastable to a common shape.

In the below example, the `numpy.add()` function is used for element-wise addition of two 2D arrays (`arr` and `arr2`). The result is a new 2D array where each element is the sum of the corresponding elements in the original arrays.

``````
# Create two multi-dimensional arrays
arr = np.array([[18, 25, 37], [5, -7, 15]])
print("First 2D array:\n", arr)
arr1 = np.array([[4, 8, 12], [-13, 24, 17]])
print("Second 2D array:\n", arr1)

# Add numpy two multi-dimensional arrays
print("Result of element-wise addition of 2D arrays:\n",arr2)

# Output:
# First 2D array:
#  [[18 25 37]
#  [ 5 -7 15]]
# Second 2D array:
#  [[  4   8  12]
#  [-13  24  17]]
# Result of element-wise addition of 2D arrays:
#  [[22 33 49]
#  [-8 17 32]]
``````

How can I add two NumPy arrays element-wise?

You can add two NumPy arrays element-wise using the `numpy.add()` function or the `+` operator. The arrays must have the same shape or be broadcastable to a common shape.

Can I add a scalar value to a NumPy array?

You can add a scalar value to a NumPy array. This operation will add the scalar value to each element of the array. You can use either the `numpy.add()` function or the `+` operator.

What happens if the shapes of two arrays are not the same?

If the shapes of two arrays are not the same, NumPy will attempt to broadcast them to a common shape. If broadcasting is not possible, a `ValueError` will be raised.

How can I add two multi-dimensional arrays in NumPy?

You can add two multi-dimensional arrays in NumPy using the `numpy.add()` function or the `+` operator. The arrays must have the same shape or be broadcastable to a common shape.

Can I add two arrays of different shapes in NumPy?

You can add two arrays of different shapes in NumPy if they are broadcastable to a common shape. Broadcasting rules will be applied to make the operation valid.

## Conclusion

In this article, I have explained the syntax and usage of `numpy.add()` function and used this function to calculate the addition of the NumPy array with examples.

Happy Learning!!