Handling Duplicate Records in a Pandas DataFrame

In this article, we will explore how to remove duplicate records from a pandas DataFrame while keeping one record based on alphabetical order.

Introduction

Pandas is a powerful library for data manipulation and analysis in Python. When working with DataFrames, it’s not uncommon to encounter duplicate records that can lead to incorrect results or data inconsistencies. In this article, we will focus on deleting duplicate records from a DataFrame while preserving one record based on alphabetical order.

The Problem

Suppose you have a DataFrame df containing duplicate records, and you want to remove the duplicates while keeping only one record for each pair. The pairs are determined by the alphabetical order of their names.

import pandas as pd
import numpy as np

# Create a sample DataFrame with duplicate records
df = pd.DataFrame([['Peter', 'Tom',1], ['Sam', 'Ed',2], ['Tom', 'Peter',1], ['Ed', 'Sam',2]], 
                  columns=["Person 1", "Person 2", "Value"])

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

Output:

   Person 1 Person 2 Value
0    Peter      Tom     1
1       Sam      Ed     2
2      Tom     Peter     1
3      Ed       Sam     2

The goal is to remove the duplicate records and keep only one record for each pair, based on alphabetical order.

Solution

To solve this problem, we can use a combination of sorting and dropping duplicates. Here’s the step-by-step solution:

Step 1: Sort Across Columns

First, we sort across columns using np.sort. This will ensure that the records are in alphabetical order based on their names.

# Import necessary libraries
import numpy as np

# Create a sample DataFrame with duplicate records
df = pd.DataFrame([['Peter', 'Tom',1], ['Sam', 'Ed',2], ['Tom', 'Peter',1], ['Ed', 'Sam',2]], 
                  columns=["Person 1", "Person 2", "Value"])

# Sort across columns using np.sort
df_sorted = np.hstack((np.sort(df.iloc[:, :-1].values, axis=1),
                       df['Value'].values[:, None]))

Step 2: Create a New DataFrame

Next, we create a new DataFrame res by combining the sorted values with their corresponding values.

# Create a new DataFrame res
res = pd.DataFrame(df_sorted, columns=df.columns)

Step 3: Drop Duplicate Records

Finally, we drop duplicate records from the new DataFrame using drop_duplicates.

# Drop duplicate records from res
res = res.drop_duplicates()

Combine the Code

Here’s the complete code that solves the problem:

import pandas as pd
import numpy as np

def delete_duplicate_records():
    # Create a sample DataFrame with duplicate records
    df = pd.DataFrame([['Peter', 'Tom',1], ['Sam', 'Ed',2], ['Tom', 'Peter',1], ['Ed', 'Sam',2]], 
                      columns=["Person 1", "Person 2", "Value"])

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

    # Sort across columns using np.sort
    df_sorted = np.hstack((np.sort(df.iloc[:, :-1].values, axis=1),
                           df['Value'].values[:, None]))

    # Create a new DataFrame res
    res = pd.DataFrame(df_sorted, columns=df.columns)

    # Drop duplicate records from res
    res = res.drop_duplicates()

    print("\nDataFrame after removing duplicates:")
    print(res)

# Execute the function
delete_duplicate_records()

Output:

Original DataFrame:
   Person 1 Person 2 Value
0    Peter      Tom     1
1       Sam      Ed     2
2      Tom     Peter     1
3      Ed       Sam     2

DataFrame after removing duplicates:
  Person 1 Person 2 Value
0    Peter      Tom     1
1       Ed      Sam     2

Conclusion

In this article, we explored how to remove duplicate records from a pandas DataFrame while keeping one record based on alphabetical order. We used a combination of sorting and dropping duplicates to achieve this goal. The code is concise and easy to understand, making it suitable for data analysts and scientists working with DataFrames in Python.

Additional Tips

When dealing with large DataFrames, it’s essential to optimize the sorting process using np.sort to avoid performance issues.
You can modify the solution to accommodate additional columns by adding them to the np.hstack function.
For more complex scenarios, consider using advanced techniques like grouping and merging to handle duplicate records.

By following this article, you’ll be able to efficiently remove duplicate records from a pandas DataFrame while preserving one record based on alphabetical order.

Last modified on 2024-02-29