Sorting Only Specific Columns from a Pandas DataFrame

When working with large datasets, it’s common to have multiple columns that need to be sorted differently. In this article, we’ll explore how to sort only specific columns from a pandas DataFrame while keeping others unchanged.

Introduction

Pandas is a powerful library in Python for data manipulation and analysis. One of its most useful features is the ability to sort DataFrames by one or more columns. However, there are situations where you want to sort only specific columns and keep others as they are. This article will show you how to achieve this using pandas.

Understanding the Problem

The question posed in the Stack Overflow post illustrates a common issue when trying to sort only specific columns from a DataFrame. The error KeyError: ('col1', 'col2') occurs because the sort_values function is attempting to sort on multiple columns at once, but the column names are not valid.

Solution Overview

To solve this problem, we’ll use the apply function in combination with the np.sort function to create a new sorted DataFrame for only the specified columns. We’ll then assign this new DataFrame back to the original DataFrame using slicing and assignment.

Step 1: Create Sample Data

First, let’s create a sample DataFrame with 20 columns (although we’ll be working with only two specific columns) to demonstrate our approach.

import pandas as pd
import numpy as np

group2 = pd.DataFrame({
    'Col1': list('nyynny'),
    'Col2': [1, 3, 5, 7, 2, 0],
    'Col3': [4, 5, 4, 5, 5, 4],
    'Col4': [7, 8, 9, 4, 2, 3],
    'Col5': [5, 3, 6, 9, 2, 4],
    'Col6': list('aaabbb')
})

Step 2: Define the Columns to Sort

Next, we’ll define the columns that we want to sort. In this case, we only need to specify Col1 and Col2.

columns_to_sort = ['Col1', 'Col2']

Step 3: Create a New Sorted DataFrame for Only Specified Columns

We’ll use the apply function to create a new sorted DataFrame for only the specified columns. The np.sort function is used to sort each column individually.

group2_sorted = group2[columns_to_sort].copy()
group2_sorted['Col1'] = np.sort(group2_sorted['Col1'])
group2_sorted['Col2'] = np.sort(group2_sorted['Col2'])

Step 4: Assign the New Sorted DataFrame Back to the Original DataFrame

Finally, we’ll assign the new sorted DataFrame back to the original DataFrame using slicing and assignment.

group2[columns_to_sort] = group2_sorted.values

Step 5: Verify the Result

To verify that our approach worked correctly, let’s print out the resulting DataFrame.

print(group2)

This should output the original DataFrame with only Col1 and Col2 sorted:

   Col1  Col2  Col3  Col4  Col5 Col6
0     n      0     4     7     5    a
1     n      1     5     8     3    a
2     n      2     4     9     6    a
3     y      3     5     4     9    b
4     y      5     5     2     2    b
5     y      7     4     3     4    b

Conclusion

In this article, we demonstrated how to sort only specific columns from a pandas DataFrame while keeping others unchanged. By using the apply function in combination with the np.sort function, we created a new sorted DataFrame for only the specified columns and then assigned it back to the original DataFrame using slicing and assignment.

Additional Considerations

When working with large datasets, it’s essential to consider memory efficiency when sorting only specific columns. In this case, we used the .copy() method to create a copy of the sorted DataFrame before assigning it back to the original DataFrame. This ensures that the original DataFrame remains unchanged.

Additionally, if you need to sort on multiple columns, you can modify the columns_to_sort list accordingly. However, be aware that this may impact performance and memory usage for larger datasets.

Last modified on 2024-04-15