Handling Duplicate Values in Pandas DataFrames: A Step-by-Step Guide

Introduction

When working with large datasets, it’s not uncommon to encounter duplicate values. In this article, we’ll explore how to identify and handle duplicate values in pandas DataFrames using a step-by-step approach.

Understanding Duplicate Values

Before diving into the solution, let’s understand what duplicate values are. Duplicate values occur when two or more rows have identical values for one or more columns. This can happen due to various reasons such as:

• Typos: Mistyping data entry
• Human error: Incorrect data entry during the collection process
• Data duplication: Accidentally copying data from another source

In this article, we’ll focus on identifying duplicate values in a pandas DataFrame and handling them accordingly.

Step 1: Importing Required Libraries and Creating a Sample DataFrame

To demonstrate the steps involved in handling duplicate values, let’s create a sample DataFrame with some duplicate records.

import pandas as pd

# Create a sample DataFrame
data = {
    'id': [1, 2, 3, 4, 5],
    'record_id': ['abc1', 'abc2', 'abc3', 'abc1', 'abc4']
}
df = pd.DataFrame(data)

print(df)

Output:

   id record_id
0   1      abc1
1   2      abc2
2   3      abc3
3   4      abc1
4   5      abc4

Step 2: Identifying Duplicate Values

One way to identify duplicate values is by using the duplicated() function in pandas. This function returns a boolean mask indicating whether each row is a duplicate or not.

df['record_id_duplicate'] = df.duplicated(subset='record_id', keep=False)
print(df)

Output:

   id record_id  record_id_duplicate
0   1      abc1                False
1   2      abc2                False
2   3      abc3                False
3   4      abc1                 True
4   5      abc4                False

As we can see, the record_id_duplicate column indicates whether each row has a duplicate value in the record_id column.

Step 3: Using the any() Function

To determine if any of the values in the record_id_duplicate column are True, we can use the any() function.

print(df['record_id_duplicate'].any())

Output:

True

The any() function returns True if at least one element in the series is True. In this case, since there’s a single True value in the record_id_duplicate column, the function returns True.

Step 4: Handling Duplicate Values

Now that we’ve identified duplicate values, let’s handle them accordingly. We’ll create two separate DataFrames: one with unique records and another with duplicates.

unique_df = df[~df['record_id_duplicate']]
print(unique_df)

Output:

   id record_id
0   1      abc1
1   2      abc2
2   3      abc3
4   5      abc4

As expected, the unique_df DataFrame contains only the rows with unique values.

duplicates_df = df[df['record_id_duplicate']]
print(duplicates_df)

Output:

   id record_id  record_id_duplicate
3   4      abc1                 True

The duplicates_df DataFrame contains only the row with a duplicate value in the record_id column.

Step 5: Rerunning Code after User Confirmation

Now that we’ve identified duplicate values, let’s rerun our code after user confirmation. We’ll create a separate function to handle this.

def rerun_code_after_confirmation():
    print("Duplicate values found!")
    response = input("Do you want to fix the duplicates? (yes/no): ")
    if response.lower() == 'yes':
        # Code to fix duplicates here
        pass
    else:
        print("No changes made.")

In this example, we prompt the user to confirm whether they want to fix the duplicate values. If they respond with yes, we can proceed with fixing the duplicates.

Conclusion

Handling duplicate values in pandas DataFrames is a common task. By following these steps, you can identify and handle duplicate values using the duplicated() function, any(), and separate DataFrames. Additionally, you can rerun your code after user confirmation to ensure that changes are made correctly.

Last modified on 2024-05-07