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
# Using add() function
arr = [2, 4, 6, 8, 12, 14]
arr2 = np.add(arr, 12)
# Example 2: Use numpy.add() function
# To add two numbers
arr = np.array(12)
arr1 = np.array(25)
arr2 = np.add(arr, arr1)
# Example 3: Use numpy add() function
# With two input arrays
arr = np.array([2, 6, 9])
arr1 = np.array([5, 8, 12])
arr2 = np.add(arr, arr1)
# Example 4: Use numpy.ndarray.add() function
arr2 = arr.__add__(arr1)
# 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]])
arr2 = np.add(arr, arr1)
```

## 2. Syntax of NumPy add() Function

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

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

### 2.1 Parameters of add()

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)
# Using add() function
# Get addition values
arr2 = np.add(arr, 12)
print("Result of addition values:\n", arr2)
```

Yields below output.

## 5. Use numpy.add() Function to Add Two Numbers

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)
# Use numpy.add() function to add two numbers
arr2 = np.add(arr, arr1)
print("Result of adding two numbers:",arr2)
```

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)
# Use numpy add() function
# With two input arrays
arr2 = np.add(arr, arr1)
print("Result of element-wise addition:",arr2)
# Output:
# First array: [2 6 9]
# Second array: [ 5 8 12]
# Result of element-wise addition: [ 7 14 21]
```

## 7. Use numpy.ndarray.**add**() Function

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.

```
# Use numpy.ndarray.add() function
arr2 = arr.__add__(arr1)
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
# For element-wise addition
arr2 = arr + arr1
print("Result of element-wise addition:",arr2)
```

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
arr2 = np.add(arr, arr1)
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]]
```

## Frequently Asked Questions

**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!!

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