Adventures in Machine Learning

Mastering Pandas: How to Change the Order of Columns in a DataFrame

Changing the Order of Columns in Pandas DataFrame: A Comprehensive Guide

When working with data, it’s essential to have control over how it’s structured and arranged. Oftentimes, you might need to reorder columns in a Pandas DataFrame to make sense of the data and perform your desired analysis.

A Pandas DataFrame is a two-dimensional data structure that organizes your data into rows and columns. It provides you with a powerful tool to manipulate and analyze your data.

In this article, we will explore how you can change the order of columns in a Pandas DataFrame. We will take you through the steps of data preparation, creating the DataFrame, and changing the order of columns in the DataFrame.

Finally, we will show you the result of changing the column order.

Data Preparation

Before we get into creating the DataFrame, it’s important to note that data preparation is crucial for proper data analysis. This step involves cleaning, filtering, and transforming the data to make it usable for your analysis.

You can prepare your data in different ways, depending on your specific needs, such as by removing duplicates, dealing with missing values, or renaming columns.

Creating the DataFrame

Once you have prepared your data, you can create a Pandas DataFrame. You can create a new DataFrame by reading in a CSV file, querying a database, or even manually entering data.

Here’s a code snippet that shows how to create a simple DataFrame:

import pandas as pd

data = {‘Name’: [‘John’, ‘Mary’, ‘Jane’], ‘Age’: [25, 30, 28], ‘City’: [‘New York’, ‘Los Angeles’, ‘Chicago’]}

df = pd.DataFrame(data)

In this example, we used a Python dictionary to create a DataFrame. The keys of the dictionary correspond to the column names, while the values in the lists correspond to the data in each column.

We then passed the dictionary to the pd.DataFrame() method to create our DataFrame.

Changing the Order of Columns

Now that we have created the DataFrame, let’s look at how you can change the order of columns. By default, the DataFrame columns are in the order in which they were added.

However, you might want to rearrange the columns for better analysis. Here’s an example of how to change the order of columns in a DataFrame:

df = df[[‘Name’, ‘City’, ‘Age’]]

In this example, we used double brackets to access specific columns.

We passed a list of column names in the order we wanted, and the new DataFrame was created with the columns in the desired order.

Original Order of Columns in DataFrame

Before we move on to changing the order of columns, let’s take a quick look at the original order of the columns in our sample DataFrame. In our example, the original order of columns is Name, Age, and City.

Changing the Order of Columns in the DataFrame

To change the order of columns in the DataFrame, we will use the same example as above. Let’s say we need to analyze the data by the city first, followed by the name and age.

To do this, we need to move the ‘City’ column to the first position, followed by ‘Name’ and ‘Age’. Here’s a code snippet that shows how to do this:

df = df[[‘City’, ‘Name’, ‘Age’]]

In this example, we changed the position of the ‘City’ column, which appeared last in the original DataFrame, to the first position.

Then, we rearranged the positions of the ‘Name’ and ‘Age’ columns, respectively, to produce the new column order.

Result of Changing Column Order

After we rearranged the columns of the DataFrame, we might want to check the result to confirm that the column order has indeed been changed. Let’s use a simple print statement to display the first five rows of the DataFrame:


The result of this print statement would show the first five rows of the DataFrame, with the ‘City’ column in the first position, followed by the ‘Name’ and ‘Age’ columns.


In conclusion, changing the order of columns in a Pandas DataFrame can be a useful technique when you need to rearrange your data for better analysis. It’s a straightforward process that involves creating a DataFrame, accessing the specific columns that need to be rearranged, and passing them to a new DataFrame in the desired order.

By following the simple steps outlined in this guide, you can easily change the order of columns in your Pandas DataFrame and achieve your desired analysis results.

Link to Additional Python Tutorials

Python is a versatile programming language used in a wide range of applications such as data analysis, machine learning, web development, and automation. If you’re new to Python, you might want to take some additional tutorials to improve your skills and deepen your understanding of the language.

Here are some great additional Python tutorials that are available online:

1. Codecademy

Codecademy offers a comprehensive and interactive Python tutorial that covers everything from basic syntax to complex concepts like functions, loops, and classes.

The tutorial is interactive, engaging, and includes practical exercises that help you learn by doing. 2.

Learn Python

Learn Python is a free online Python tutorial that covers all essential aspects of Python programming. The tutorial is perfect for beginners who have no prior experience with programming.

It includes comprehensive lessons on basic syntax, data structures, functions, and more. 3.

Python Official Documentation

The Python Official Documentation is an excellent resource for learning Python. The site provides documentation on the Python language, various Python libraries, and modules.

You can find detailed explanations of Python concepts, syntax, examples, and use cases. 4.


Coursera offers several Python courses that range from beginner to advanced levels. The courses are designed to provide hands-on experience to students and include coding assignments that help students apply what they’ve learned.

You can choose from a range of Python courses, such asto Data Science in Python, Applied Data Science with Python, and Python Data Structures. 5.

MIT OpenCourseWare

MIT OpenCourseWare offers several Python courses that are free and open to everyone. The courses cover different aspects of Python programming, including data science, machine learning, and algorithms.

The website provides lecture notes, assignments, and other resources to help you learn Python. In conclusion, these are just a few of the many additional Python tutorials available online.

With the help of these resources, you can deepen your understanding of Python programming, acquire new skills, and advance your career. Whether you’re a beginner or an experienced programmer, taking additional Python tutorials can help you improve your skills, learn new concepts, and stay up-to-date with the latest trends in the field.

So, check out these tutorials and start improving your Python programming skills today!

In this article, we discussed the importance of changing the order of columns in a Pandas DataFrame and provided step-by-step instructions on how to accomplish this task. We also provided links to additional Python tutorials to help readers deepen their Python knowledge and skills.

The ability to manipulate and analyze data is crucial in various fields, and Pandas provides a powerful tool to achieve it. By reordering columns, you can better analyze data and uncover insights that might have been missed otherwise.

Python is also a versatile language that can help you in various ways, whether it be automating tasks, analyzing data, or developing web applications. By improving your Python skills through additional tutorials, you can become a more proficient programmer and advance your career.

Remember, learning is an ongoing process, and it’s never too late to improve your skills. So, keep learning, practicing, and applying what you’ve learned to continue growing as a programmer.

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