**The most common issue that Python programmers get into is how to shuffle two or multiple numpy arrays together. If you fall into this category, don’t worry; in this post, we will explain in detail how to shuffle two arrays. Continue reading. 🧾**

In this article, we will **shuffle two different NumPy arrays** with the same length but in a different order. Shuffling two NumPy arrays in Python means reordering the elements of both arrays in the same pattern.

Thus, in this tutorial, you will learn different ways to shuffle two given NumPy arrays, and we will make sure you understand all the ways with the help of simple examples. First, we understand what NumPy is and how we can use it.

So let’s get started on the solutions right away.

##### Table of Contents

## What is NumPy in Python, and How to Use it?

Nearly all branches of research and engineering use the free source Python library known as **NumPy** (Numerical Python). It is the de facto standard for manipulating numerical data in Python and forms the basis of both the **PyData **and **scientific Python ecosystems**.

There are other different libraries that Python programmers can use in **Data Science**, **Artificial Intelligence**, and **Scientific programs**, including **Pandas, SciPy, Matplotlib, scikit-learn, **and** scikit-image**, which make substantial use of the NumPy API.

Although Python lists can be an alternative to arrays, they need to improve performance when processing vast quantities of numerical data. We use a Python module called NumPy to solve this problem. Numerical Python is the full name of the term NumPy.

The array object** ndarray(N dimension array)** is provided by NumPy. They are comparable to typical Python sequences but differ in a few significant ways. Python itself is the only need for installing NumPy. You must use **pip to install NumPy**, and to do so; you have to enter the following:

**Code**

pip install numpy

After installing numpy, you can easily use it. Here is a simple example of using numpy in your code:

**Code**

# import numpy first, then you can use it import numpy as np # declare and initialize a simple array myArray = [10, 20, 30, 40] # Declare and initialize a numpy array nArray = np.array(myArray) # Print numpy on screen print(nArray)

**Output**

[10 20 30 40]

## Different Ways to Shuffle Two NP Arrays Together

Following are the ways to shuffle two NumPy arrays together:

- Using
**random Generator permutation**. - Using
**numpy random permutation**. - Using
**sklearn.utils.shuffle()**function. - Using
**random shuffle**function.

### Method 1: Using Random Generator Permutation

The **permutation **method of the NumPy Random Generator creates a new array with the values shuffled. Let’s understand how it works with the help of a simple example:

**Code**

#import numpy array import numpy as np # Declare and initialize two arrays array1 = np.array([[8, 1], [2, 3], [4, 5], [6, 7]]) array2 = np.array([10, 20, 30, 40]) print("Both array before shuffling") print("Array 1 : ", array1) print("Array 2 : ", array2) # Create a new Generator with the default BitGenerator myGenerator = np.random.default_rng() # Print shuffled array on screen print("Both arrays after shuffling") print("Array 1 : ", myGenerator.permutation(array1)) print("Array 2 : ", myGenerator.permutation(array2))

**Output**

Both array before shuffling Array 1 : [[8 1] [2 3] [4 5] [6 7]] Array 2 : [10 20 30 40] Both arrays after shuffling Array 1 : [[2 3] [8 1] [6 7] [4 5]] Array 2 : [30 40 10 20]

### Method 2: Using Random Permutation

We may obtain random samples of permutation and return sequences using the **numpy.random.permutation()** method. With the help of this function, we can easily shuffle two numpy arrays together. Let’s understand with the help of a simple example:

**Code**

#import numpy array import numpy as np # Declare and initialize two arrays array1 = np.array([[8, 1], [2, 3], [4, 5], [6, 7]]) array2 = np.array([10, 20, 30, 40]) # store random permutation to a variable myshuffler = np.random.permutation(len(array1)) # Use square notation to shuffle array shuffledArray1 = array1[myshuffler] shuffledArray2 = array2[myshuffler] # Print shuffled array on screen print(shuffledArray1) print(shuffledArray2)

**Output**

[[6 7] [8 1] [2 3] [4 5]] [40 10 20 30]

### Method 3: Using sklearn.utils.shuffle() Function

Using the **shuffle()** method from the **sklearn.utils** module is an alternative strategy for solving the given problem. Arrays and sparse matrices can be consistently shuffled using the shuffle method. The two arrays can be passed as **sklearn.utils.shuffle(array1, array2)**, which properly shuffles them before returning a shuffled version of each array.

You can easily understand this concept with the help of the following example:

**Code**

#import numpy array and sklearn import numpy as np import sklearn # Declare and initialize two arrays array1 = np.array([[8, 1], [2, 3], [4, 5], [6, 7]]) array2 = np.array([10, 20, 30, 40]) print("Both array before shuffling") print("Array 1 : ", array1) print("Array 2 : ", array2) # Shuffle both arrays together array1, array2 = sklearn.utils.shuffle(array1, array2) # Print shuffled array on screen print("Both arrays after shuffling") print("Array 1 : ", array1) print("Array 2 : ", array2)

**Output**

Both array before shuffling Array 1 : [[8 1] [2 3] [4 5] [6 7]] Array 2 : [10 20 30 40] Both arrays after shuffling Array 1 : [[2 3] [8 1] [4 5] [6 7]] Array 2 : [20 10 30 40]

### Method 4: Using Random Shuffle Method

The shuffle method from the **numpy.random** module is another comparable function that enables you to shuffle the specified arrays. It accepts a sequence as an input and rearranges its elements to produce the original sequence in a new order. Keep in mind that the original sequence itself is altered by the shuffle method.

Let’s understand how the shuffle method will work with the help of a simple example:

**Code**

#import numpy array import numpy as np # Declare and initialize two arrays array1 = np.array([[8, 1], [2, 3], [4, 5], [6, 7]]) array2 = np.array([10, 20, 30, 40]) print("Both array before shuffling") print("Array 1 : ", array1) print("Array 2 : ", array2) # Shuffle both arrays with random shuffle function shuffler = np.arange(len(array1)) np.random.shuffle(shuffler) array1 = array1[shuffler] array2 = array2[shuffler] # Print shuffled array on screen print("Both arrays after shuffling") print("Array 1 : ", array1) print("Array 2 : ", array2)

**Output**

Both array before shuffling Array 1 : [[8 1] [2 3] [4 5] [6 7]] Array 2 : [10 20 30 40] Both arrays after shuffling Array 1 : [[4 5] [6 7] [2 3] [8 1]] Array 2 : [30 40 20 10]

#### Conclusion

To conclude the article on “**how to shuffle two np arrays together**“, we discussed four different methods. We can shuffle NumPy arrays with the help of **shuffle **and **permutation **methods. The main distinction between **permutation **and **shuffle **functions is that permutation returns a new array while the original array is left unchanged. Shuffle, however, altered the original array and did not produce a new one.

This post discusses both approaches in-depth and provides perfect examples to help you understand the subject.

**Share this article with your fellow programmers if you found it helpful, and let us know in the comments area below 👇 which way worked best for you to solve the puzzle of shuffling two np arrays.**

**Happy Coding!🙋**