# numpy.copyto() – The NumPy copyto Python Function

## Introduction to numpy.copyto() Function

We can make a duplicate of all the data elements in a NumPy array using the Numpy numpy.copyto() function. The original NumPy array will not be affected if any data elements in the copy are changed.

Syntax:

`numpy.copyto(destination, source)`

Return: Return a copy of an array

## Code Examples of numpy.copyto() Function

Example 01: In this example, we can see how the numpy.copyto() function is use to copy elements from a source array to a destination array.

``````# import the important module in python
import numpy as np

# make an array with numpy
softhunt = np.array([1, 2, 3])
softhunt_array = [7, 3, 7]

# applying numpy.copyto() method
np.copyto(softhunt, softhunt_array)

print(softhunt)``````

Output:

`[7 3 7]`

Code explanation:

• Line 1: We import the NumPy module.
• Lines 3–4: We create input arrays, softhunt and softhunt_array, using the array() function.
• Line 7: We implement the copyto() function by copying the values of array softhunt into array softhunt_array.
• Line 9: We print the modified array softhunt.

Example 02:

``````# import the important module in python
import numpy as np

# make an array with numpy
softhunt = np.array([[1, 2, 3], [4, 5, 6]])
softhunt_array = [[9, 8, 7], [6, 5, 4]]

# applying numpy.copyto() method
np.copyto(softhunt, softhunt_array)

print(softhunt)``````

Output:

```[[9 8 7]
[6 5 4]]```

## FAQs

### What does NumPy () do in Python?

NumPy may be use to conduct a wide range of array-based mathematical operations. It extends Python with powerful data structures that ensure fast computations with arrays and matrices. As well as a large library of high-level mathematical functions that work with these arrays and matrices.

### Why do we need NumPy?

NumPy arrays are more compact and quicker than Python lists. An array uses less memory and is easier to work with. NumPy stores data in a significantly less amount of memory and has a way of selecting data types.

### What is the use of NumPy in machine learning?

The abbreviation NumPy stands for “Numerical Python”. It is a Python library that may be used to perform a variety of mathematical and scientific activities. It includes multi-dimensional arrays and matrices as well as a number of high-level mathematical functions that work on them. That’s why it is important in machine learning.