How to use NumPy to swap rows of an array in Python?

This recipe explains how to use NumPy to swap rows of an array in Python.

Swapping rows in a NumPy array is a fundamental operation when working with structured data. It allows us to rearrange data for various purposes, such as sorting or reshaping. In this short guide, we'll explore how to efficiently swap rows in a NumPy array, a skill that's crucial for tasks like data preprocessing and feature engineering. With NumPy's array manipulation capabilities at your disposal, you'll quickly grasp this essential technique.

Learn how to build Regression (Linear,Ridge,Lasso) Models in NumPy Python 

How to swap two rows of an array in Python?

Let us begin with how to swap rows in 2D array Python tutorial:

Step 1: Import NumPy Library

In the first step, you import the NumPy library in your Python script or Jupyter Notebook. NumPy is a popular Python library that provides a wide range of tools for working with arrays, making it an essential asset for various data manipulation and mathematical operations.

import numpy as np

Here, you've imported NumPy and aliased it as "np" for convenience.

Step 2: Create Your NumPy Array

Next, you define a random NumPy array in Python. This array serves as your data source and can contain multiple rows and columns. Here's an example array with four rows and three columns:

a = np.array([[4, 3, 1],

              [5, 7, 0],

              [9, 9, 3],

              [8, 2, 4]])

In this example, you've created a 4x3 array containing various values. This is the data that you'll work with in the subsequent steps.

Step 3: How to swap two rows in a 2D NumPy array

Now, you perform row transformation to swap specific rows in the NumPy array. In this example, you swap row 0 with row 2, effectively reordering the data:

a[[0, 2]] = a[[2, 0]]

This code snippet swaps the entire content of row 0 with that of row 2, and vice versa.

Step 4: Display the Rounded Array

The resulting array has a new row order, as follows:

print(a)

Output:

array([[9, 9, 3],

       [5, 7, 0],

       [4, 3, 1],

       [8, 2, 4]])

By following these steps, you chave learnt how to swap rows in numpy array, a valuable operation for data manipulation, rearranging datasets, and various analytical tasks.

Scroll till the end to see the output.

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In this guide, we've covered the process of swapping rows in NumPy array, a skill that's indispensable in data manipulation and analysis. Whether you're reorganizing data for specific algorithms or reshaping your dataset, the ability to swap rows efficiently is a valuable asset. NumPy simplifies this process and equips you with the tools needed to tackle more complex data tasks. We highly suggest you continue exploring NumPy's features to enhance your data handling skills and advance your proficiency in data analysis and science by working on practical projects. And if you are on a hunt for hands-on projects in Data Science and Big Data, we highly recommend checking out ProjectPro. ProjectPro has an exclusive repository of solved projects in data science and big data that you must try if you are aiming to hone relevant skills in the two exciting domains of Artificial Intelligence

 

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