5 Best Ways to Remove First Duplicate Rows in a Python DataFrame

💡 Problem Formulation: When working with DataFrames in Python, you may often encounter duplicate rows that need to be removed to maintain the integrity of your dataset. Consider a DataFrame where each row represents an individual record. The challenge is to remove only the first instance of any duplicate rows, leaving subsequent duplicates intact. For example, if rows 2, 4, and 6 are identical, only row 2 should be removed.

Method 1: Using DataFrame.drop_duplicates()

The drop_duplicates() function in pandas offers a straightforward approach to remove duplicate rows from a DataFrame. By default, drop_duplicates() retains the first occurrence and removes subsequent duplicates. However, when combined with the keep='last' parameter, it does the opposite; it preserves the last occurrence and removes earlier ones. By inverting this DataFrame, applying drop_duplicates(), and then inverting it back, you can effectively remove the first instances of duplicates.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'A': [1, 1, 2, 2, 3],
    'B': [5, 5, 6, 6, 7]
})

# Remove the first occurrences of duplicates
df_reversed = df[::-1]
df_unique = df_reversed.drop_duplicates(keep='last')
df_result = df_unique[::-1]

print(df_result)

The output of this code is:

This code snippet creates a DataFrame and then reverses it. drop_duplicates() is used with the keep='last' argument to keep the last occurrence of each duplicate. After removing the duplicates, reversing the DataFrame a second time restores the original order, less the first instances of any duplicates.

Method 2: Using DataFrame.duplicated() with Inverted Indexing

The DataFrame.duplicated() method returns a boolean Series indicating whether each row is a duplicate or not. By identifying rows that are marked as duplicates and have not been seen before, you can create a mask that filters out the first occurrences while keeping subsequent ones. Applying this mask with inverted indexing enables the removal of the first duplicates.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'A': [1, 1, 2, 2, 3],
    'B': [5, 5, 6, 6, 7]
})

# Track seen duplicates
seen = set()
mask = [not(item in seen or seen.add(item)) if is_dup else True
        for item, is_dup in zip(df.itertuples(index=False, name=None), df.duplicated())]

# Remove first occurrences of duplicates
df_result = df[mask]

print(df_result)

The output of this code is:

This code snippet iterates over each row while checking if the row has been duplicated. If a row is a duplicate and has not been encountered before (not in the seen set), it is marked for removal. This intelligent filtering ensures only the first duplicates are removed, and subsequent duplicates are retained.

Method 3: Iterative Removal of First Duplicates

In scenarios where performance is not a primary concern and DataFrame sizes are relatively small, you can iteratively remove the first duplicate encountered. This method is straightforward but can be inefficient for larger DataFrames due to the cumulating cost of iteratively updating the DataFrame.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'A': [1, 1, 2, 2, 3],
    'B': [5, 5, 6, 6, 7]
})

# Iteratively remove first duplicates
for i in df.index:
    if df.loc[i].duplicated(keep=False).any() and not df.loc[:i].duplicated().any():
        df = df.drop(i)

print(df)

The output of this code is:

This code snippet loops through the DataFrame by index, checking each row for duplication. If a row is the first duplicate, it is dropped from the DataFrame. This method may be useful for small datasets but is not recommended for larger ones due to the high computational cost.

Method 4: Use of Boolean Indexing with cumsum()

Boolean indexing is a powerful feature in pandas that allows for filtering DataFrames based on a boolean condition. By applying a cumulative sum (cumsum()) on the duplicates identified by duplicated(), we can construct a boolean mask that removes the first encountered duplicates.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'A': [1, 1, 2, 2, 3],
    'B': [5, 5, 6, 6, 7]
})

# Create a boolean mask
mask = ~((df.duplicated()) & (df.duplicated().cumsum() == 1))

# Use the mask to filter the DataFrame
df_result = df[mask]

print(df_result)

The output of this code is:

In this code, duplicated() is used to flag all duplicates, and cumsum() is applied to carry a running total of these flags. The boolean mask is constructed to remove rows that are duplicates and have a cumulative sum of 1 (the first occurrences). Applying this mask to the DataFrame effectively removes the targeted rows.

Bonus One-Liner Method 5: Using DataFrame.loc[] with a Custom Filter

A one-liner approach utilizes the DataFrame.loc[] indexer combined with a custom lambda function to filter out the first duplicates. This method is highly concise and can be useful as a quick solution within the interactive development process, though it may be less readable for beginners.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'A': [1, 1, 2, 2, 3],
    'B': [5, 5, 6, 6, 7]
})

# One-liner to remove first duplicates
df_result = df.loc[df.duplicated() | ~df.apply(lambda row: row.name if row.name not in df.loc[:row.name].duplicated() else False, axis=1)]

print(df_result)

The output of this code is:

This one-liner code uses a lambda function to iterate over each row, checking if it is a duplicate within the slice of the DataFrame up to the current index. If it is not, the row is kept. By leveraging the indexer df.loc[], we apply the filter and return the DataFrame with the first duplicates removed.

Summary/Discussion

Method 1: DataFrame.drop_duplicates() with DataFrame inversion. Strengths: Utilizes built-in pandas functions, provides a clear approach. Weaknesses: Involves reversing the DataFrame twice, which may be unnecessary overhead.
Method 2: DataFrame.duplicated() with Inverted Indexing. Strengths: Efficient and concise, avoids explicit DataFrame reversals. Weaknesses: Slight increase in complexity due to the custom logic for tracking seen duplicates.
Method 3: Iterative Removal. Strengths: Conceptually simple, very explicit in its logic. Weaknesses: Highly inefficient, especially for large DataFrames, due to repeated DataFrame operations.
Method 4: Use of Boolean Indexing with cumsum(). Strengths: Elegant, utilizes built-in functions for a concise solution. Weaknesses: The approach might require additional understanding of boolean indexing and cumsum behavior.
Bonus Method 5: One-Liner with DataFrame.loc[] and lambda. Strengths: Compact and handy for quick manipulations. Weaknesses: Readability suffers, which may hinder debugging and maintainability.