Pandas: How to swap 2 columns in a DataFrame

Updated: February 20, 2024 By: Guest Contributor Post a comment


Pandas is one of the most popular libraries in Python, famed for its powerful data manipulation capabilities. Whether you’re handling large datasets or performing complex data analysis, Pandas stands out as a pivotal tool in the data science toolkit. Among the myriad tasks you might perform, swapping two columns in a DataFrame is a basic yet essential operation. In this tutorial, we will explore different methods to achieve this, advancing from basic techniques to more complex scenarios.

Understanding DataFrames

Before we dive into column swapping, it’s vital to understand the core concept of the DataFrame. A DataFrame in Pandas is a two-dimensional, size-mutable, and heterogeneous tabular data structure with labeled axes (rows and columns). It can be thought of as a dict-like container for Series objects. This makes it a convenient tool for data analysis tasks.

Basic Column Swapping

Let’s start with the most straightforward method of swapping columns. Assume we have the following DataFrame:

import pandas as pd

# Sample DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'City': ['New York', 'Paris', 'London']}
df = pd.DataFrame(data)

This will output:

      Name  Age      City
0    Alice   25  New York
1      Bob   30    Paris
2  Charlie   35   London

To swap the ‘Age’ and ‘City’ columns, we can use the following method:

df = df[['Name', 'City', 'Age']]
      Name      City  Age
0    Alice  New York   25
1      Bob    Paris   30
2  Charlie   London   35

This method involves manually rearranging the column names in the list. It’s the simplest approach and works fine for DataFrames with a small number of columns.

Using the .reindex() Method

The .reindex() method allows more flexibility and can be particularly useful for larger DataFrames. To swap columns using this method, you can specify the new order of the columns:

new_order = ['Name', 'City', 'Age']
df = df.reindex(columns=new_order)

As seen above, .reindex() achieves the same result as manually listing the column names, but it’s more readable and maintainable, especially when dealing with many columns.

Using Column Indexes

If you prefer to use column indexes instead of names, Pandas allows this as well. This method is handy when the column names are long or if you’re working with datasets where columns are referred to by their position. Consider the following example:

df = df[df.columns[[0, 2, 1]]]
      Name  Age      City
0    Alice   25  New York
1      Bob   30    Paris
2  Charlie   35   London

Here, we’ve used the DataFrame’s .columns attribute in combination with list indexing to swap the ‘Age’ and ‘City’ columns based on their position. This method is succinct and avoids hard-coding column names, making it adaptable to different DataFrames.

Advanced Swapping Techniques

For more advanced use cases, such as swapping columns while performing operations on the DataFrame, Pandas offers ample flexibility. Let’s explore a scenario where you need to swap two columns’ positions while also renaming them:

df = df.rename(columns={'Age': 'Years', 'City': 'Location'})
df = df[['Name', 'Location', 'Years']]

This two-step process involves renaming the columns first and then rearranging them. This approach is particularly useful when you need to both reorder and rename columns, streamlining the process into cohesive actions.

Swapping Without Reordering the DataFrame

In some cases, you might want to swap two columns without changing the overall order of the columns in the DataFrame. You can achieve this using the following technique:

df.columns = df.columns.tolist()
temp = df['Age']
df['Age'] = df['City']
df['City'] = temp

This method uses a temporary variable to hold one column’s data while swapping the values. It’s a more manual approach but provides precise control over the swapping process without rearranging other columns.


Swapping columns in a Pandas DataFrame is a versatile operation that can be achieved through various methods, depending on the specificity of your task and your personal coding preference. Whether through direct listing, using methods like .reindex(), or manipulating column indexes, Pandas caters to a wide array of scenarios. Developing a familiarity with these techniques empowers you to handle DataFrames more effectively, making your data analysis tasks more efficient and intuitive.