Dropping Duplicates within Groups Only

=====================================================

In the world of data analysis and manipulation, dropping duplicates from a dataset can be an essential task. However, when dealing with grouped data, where each group has its own set of duplicate rows, things can get more complicated. In this article, we’ll explore how to drop duplicates within groups only using the pandas library in Python.

Problem Statement

The problem at hand is to remove duplicate rows from a DataFrame, but only within each specific “spec” group in column ‘A’. The entire DataFrame should remain intact, and any row that is duplicated across different groups should be preserved if it falls under a subsequent “spec” entry.

For example, consider the following DataFrame:

In this case, we want to drop duplicates only within each “spec” group. For instance, the row ’test text1 second’ should be removed because it’s a duplicate of ’text1 second’, but it should still exist in the resulting DataFrame if it falls under the next “spec” entry.

Solution Overview

The solution involves using the groupby function from pandas to group the data by the “spec” column and then applying the duplicated method to each group. We’ll use this result to filter out duplicate rows within each group while preserving those that fall under subsequent “spec” entries.

Step 1: Grouping Data

To begin, we need to group the data by the values in the “A” column where it equals ‘spec’. This is achieved using the groupby function along with a boolean mask created from the “eq(‘spec’)” condition. The result will be a Series that holds the cumulative sum of these indices.

df.groupby(df.A.eq('spec').cumsum())

Step 2: Identifying Duplicates

Next, we’ll apply the duplicated method to each group in the DataFrame to identify duplicate rows. The apply function is used to operate on each group independently.

df.groupby(df.A.eq('spec').cumsum()).apply(lambda x: x.duplicated())

Step 3: Retaining Non-Duplicate Rows

Finally, we’ll use a boolean mask created from the results of the duplicated operation to retain only those rows that are not duplicates. This is achieved by setting the values to be False (indicating non-duplicates) and then indexing into the DataFrame using this mask.

df[~df.groupby(df.A.eq('spec').cumsum()).apply(lambda x: x.duplicated()).values]

Example Usage

Here’s how you might use this approach in practice:

import pandas as pd

# Create a sample DataFrame
data = {
    'A': ['test', 'act', 'act', 'test', 'act', 'test', 'act', 'test', 'act', 'act', 'test', 'act'],
    'B': ['text1', 'text12', 'text14', 'text32', 'text34', 'text85', 'text87', 'text1', 'text12', 'text14', 'text85', 'text87'],
    'C': ['second', 'text13', 'text15', 'text33', 'text35', 'text86', 'text88', 'text2', 'text13', 'text15', 'text86', 'text88']
}
df = pd.DataFrame(data)

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

# Drop duplicates within groups only
df_no_duplicates = df[~df.groupby(df.A.eq('spec').cumsum()).apply(lambda x: x.duplicated()).values]

print("\nDataFrame after dropping duplicates within groups only:")
print(df_no_duplicates)

This approach provides an efficient way to handle grouped duplicate rows in a DataFrame while preserving those that are unique across different “spec” entries.

Last modified on 2025-03-29