Grouping Duplicate Pairs in a Pandas DataFrame

Reducing duplicate values by pairs in Python

When working with dataframes, it’s not uncommon to encounter duplicate values that can be paired together. In this article, we’ll explore how to reduce these duplicate values in a pandas dataframe while keeping the original unique values intact.

Introduction

Before diving into the solution, let’s understand what kind of problem we’re dealing with. Imagine having a dataframe where each row represents a pair of values, and we want to keep only one of the paired values while reducing the other to zero. This problem is particularly relevant in fields like finance, where duplicate pairs might represent errors or outliers.

The Challenge

The original pseudo-code provided attempts to solve this problem by iterating over each pair of rows element-wise. However, this approach has a significant drawback: it’s slow and inefficient for large dataframes. Moreover, the existing solution has limitations when dealing with repeated values that aren’t necessarily neighbor pairs, as well as zero values that need to be kept in unique pairs.

The Solution

Fortunately, we can leverage pandas’ grouping capabilities to achieve this goal more efficiently. In this article, we’ll explore a new approach using groupby and drop_duplicates.

Step 1: Grouping by Duplicate Pairs

df_grouped = df.groupby(df.ne(df.shift(axis=1)).all(axis=0).cumsum(), axis=1)

Here, we’re using the ne function to check for non-equal values between consecutive rows (i.e., the pairs) and the shift function to shift each row one position forward. The resulting boolean mask is then used to create a cumulative sum of unique pairs.

Step 2: Applying drop_duplicates

df_unique = df_grouped.apply(lambda x: x.drop(x.columns[1::2], axis=1))

Next, we’re applying the drop function with a subset of columns (odd-indexed columns only) to each group. This effectively removes duplicate pairs while preserving unique values in even-indexed columns.

Step 3: Dropping NaN Rows

df_unique = df_unique.dropna(axis=1)

Finally, we’re removing any rows that contain all zeros using the dropna function with axis=1.

Example Walkthrough

Let’s apply this solution to the provided example dataframe:

import pandas as pd

# Create a sample dataframe
df = pd.DataFrame({
    'A': [128.437, 129.588, 121.639],
    'B': [5100.9, 5102.05, 5029.08],
    'C': [4888.81, 4959.55, 5030.24],
    'D': [4888.81, 4889.96, 4889.96]
})

# Print the original dataframe
print("Original DataFrame:")
print(df)

# Apply the grouping and dropping solution
df_unique = df.groupby(df.ne(df.shift(axis=1)).all(axis=0).cumsum(), axis=1).apply(lambda x: x.drop(x.columns[1::2], axis=1)).dropna(axis=1)

# Print the resulting dataframe
print("\nResulting DataFrame:")
print(df_unique)

Conclusion

By utilizing pandas’ groupby and drop_duplicates functions, we can efficiently reduce duplicate pairs in a dataframe while preserving unique values. This solution is particularly useful when dealing with large dataframes or situations where manual iteration is not feasible.

The key takeaway from this article is the importance of leveraging group-by operations to simplify complex problems in pandas. With practice and experience, you’ll become proficient in using these functions to streamline your data analysis workflow.

Last modified on 2025-03-20