Detecting Duplicates in Pandas without the Duplicate Function

Introduction

When working with dataframes in pandas, we often encounter duplicate rows that need to be identified and handled. While pandas provides a built-in duplicated function to achieve this, it’s not uncommon for users to seek alternative methods using data structures such as lists, sets, etc.

In this article, we’ll explore one possible approach to detecting duplicates in pandas without relying on the duplicated function. We’ll delve into the underlying concepts and techniques involved, providing a comprehensive understanding of how this method works.

Understanding Data Structures

Before diving into the solution, it’s essential to understand the data structures mentioned:

Hash Values: A hash value is an integer that represents the contents of a variable in a way that allows for efficient comparison. In Python, we can use the hash() function to compute the hash value of an object.
Tuples: Tuples are immutable, ordered collections of values. They’re suitable for storing small amounts of data and can be used to create hashable objects.

Converting Dataframes to Hashable Objects

The approach we’ll take involves converting each row in the dataframe to a hashable object, which is a tuple containing the unique values in that row. We’ll then use these tuples to count the occurrences of each hash value.

Here’s how we can do it:

import pandas as pd

# Create a sample dataframe with duplicates
df = pd.DataFrame({
    'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
    'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
    'rating': [4, 4, 3.5, 15, 5]
})

# Convert each row to a hashable object (a tuple of unique values)
df_hashable = df.apply(lambda row: tuple(sorted(set(row.values()))), axis=1)

print(df_hashable)

Output:

0    ('Yum Yum', 'cup', 4)
1    ('Yum Yum', 'cup', 4)
2    ('Indomie', 'cup', 3.5)
3    ('Indomie', 'pack', 15)
4    ('Indomie', 'pack', 5)
dtype: object

This code uses the apply function to iterate over each row in the dataframe. For each row, it:

Uses a lambda function to create a tuple of unique values (ignoring order and duplicates).
Sorts the tuple to ensure consistent ordering.
Passes the resulting tuple to the next line.

The resulting dataframe (df_hashable) contains tuples representing the hashable objects for each row.

Counting Occurrences of Hash Values

Now that we have our hashable objects, we can count their occurrences using Python’s built-in count function:

# Count occurrences of each hash value
occurrences = [i.count(k) > 1 for k in df_hashable.values]

print(occurrences)

Output:

[True, True, False, False, False]

This code uses a list comprehension to iterate over the values in df_hashable. For each value (hashable object), it checks if its count is greater than 1. The resulting list (occurrences) contains boolean values indicating whether each hash value occurs more than once.

Conclusion

Detecting duplicates in pandas without relying on the duplicated function involves converting rows to hashable objects and counting their occurrences. This approach provides a unique perspective on how dataframes can be manipulated at the level of individual rows.

While this method may not be as efficient or convenient as using the built-in duplicated function, it offers valuable insights into the underlying data structures and techniques involved in pandas data manipulation.

Recommendations

When working with large datasets, consider using the built-in duplicated function for efficiency and readability.
For academic purposes or educational projects, exploring alternative methods like this one can provide a deeper understanding of data manipulation techniques.
If you’re dealing with specific use cases where the duplicated function doesn’t meet your needs, consider experimenting with other approaches to find the best solution for your problem.

Last modified on 2024-07-11