How to reverse the column order of the Pandas DataFrame?

Let's learn how to reverse the column order of the Pandas DataFrame using iloc and other approaches.

Using Slicing - columns[::-1]

The columns[::-1] slices the column labels in reverse order. Let's consider an example:

import pandas as pd

# Sample DataFrame
data = {
    'Name': ['Rachel', 'Monica', 'Phoebe', 'Joey', 'Chandler', 'Ross'],
    'Age': [29, 30, 31, 28, 32, 33], 
    'Occupation': ['Fashion Executive', 'Chef', 'Musician', 'Actor', 'Advertiser', 'Paleontologist']

    }

df = pd.DataFrame(data)

print("Original DataFrame:")
print(df)

reversed_df = df[df.columns[::-1]]

print("Reversed DataFrame (using columns[::-1]):")
print(reversed_df)

Output:

Original DataFrame:
       Name  Age         Occupation
0    Rachel   29  Fashion Executive
1    Monica   30               Chef
2    Phoebe   31           Musician
3      Joey   28              Actor
4  Chandler   32         Advertiser
5      Ross   33     Paleontologist

Reversed DataFrame (using columns[::-1]):
          Occupation  Age      Name
0  Fashion Executive   29    Rachel
1               Chef   30    Monica
2           Musician   31    Phoebe
3              Actor   28      Joey
4         Advertiser   32  Chandler
5     Paleontologist   33      Ross

Reversing Column Order using iloc[] with Slicing

iloc[] method slices the DataFrame by columns using positional indexing.

reversed_df = df.iloc[:, ::-1]

print("Reversed DataFrame (Using iloc with Slicing):")
print(reversed_df)

Output:

The iloc method and columns[::-1] are the most common methods used to reverse the column order of a Pandas DataFrame. Apart from these, there are also other methods available that can achieve the same outcome:

Using reversed() to reverse column order in Pandas DataFrame

The reversed() function creates a reverse iterator for the column labels.

reversed_df = df[list(reversed(df.columns))]

print("Reversed DataFrame (using reversed()):")
print(reversed_df)

Output:

Reversed DataFrame (using reversed()):
          Occupation  Age      Name
0  Fashion Executive   29    Rachel
1               Chef   30    Monica
2           Musician   31    Phoebe
3              Actor   28      Joey
4         Advertiser   32  Chandler
5     Paleontologist   33      Ross

Using reindex()

Reindexing (using reindex() method) allows you to rearrange the columns in a reversed order.

reversed_df = df.reindex(columns=df.columns[::-1])

print("Reversed DataFrame (using reindex()):")
print(reversed_df)

Output:

Reversed DataFrame (using reindex()):
          Occupation  Age      Name
0  Fashion Executive   29    Rachel
1               Chef   30    Monica
2           Musician   31    Phoebe
3              Actor   28      Joey
4         Advertiser   32  Chandler
5     Paleontologist   33      Ross

Using T (Transpose)

Transpose the DataFrame, reverse the rows (now columns), and transpose it back.

reversed_df = df.T[::-1].T

print("Reversed DataFrame (Using T (Transpose)):")
print(reversed_df)

Output:

Reversed DataFrame (Using T (Transpose)):
          Occupation Age      Name
0  Fashion Executive  29    Rachel
1               Chef  30    Monica
2           Musician  31    Phoebe
3              Actor  28      Joey
4         Advertiser  32  Chandler
5     Paleontologist  33      Ross

Each of these methods achieves the same result: reversing the column order of a Pandas DataFrame. Here’s a quick summary of the different approaches:

• The columns[::-1] method is the most straightforward and efficient for reversing columns.
• Use iloc if you prefer positional slicing.
• Both reversed() and reindex() offer clean, alternative approaches.
• While T transpose works, it is less commonly used for this purpose due to its complexity.