5 Best Ways to Remove Rows with Similar kth Column Element in Python

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💡 Problem Formulation: When working with datasets in Python, a common task is to clean up data by removing rows that have similar entries in a specific column, referred to as the kth column. For example, if we have a dataset where the 3rd column represents product IDs and we want to remove all rows with duplicate IDs, the desired output would be a dataset with unique product IDs in the 3rd column.

Method 1: Using pandas drop_duplicates

This method utilizes the pandas library, particularly the drop_duplicates function. This function is well-suited for removing duplicate rows based on one or more columns. The specification is to keep the first occurrence and discard the rest.

Here’s an example:

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3, 2],
    'B': [5, 6, 7, 6],
    'C': [9, 0, 1, 0]  
})

# Remove duplicate rows based on the second column 'B'
df_unique = df.drop_duplicates(subset='B', keep='first')
print(df_unique)

Output:

   A  B  C
0  1  5  9
1  2  6  0

This code snippet creates a DataFrame with three columns A, B, and C. We then apply drop_duplicates on column ‘B’, indicating to remove rows with similar elements in this column and keep only the first occurrence. The resulting DataFrame df_unique contains only unique values in column ‘B’.

Method 2: Using GroupBy followed by drop

The GroupBy operation groups rows that have the same value in the specified column and then we can use the drop function to discard duplicates, keeping only the first entry within each group.

Here’s an example:

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3, 2],
    'B': [5, 6, 7, 6],
    'C': [9, 0, 1, 0]  
})

# Group by the second column and keep the first occurrence
df_unique = df.groupby('B', as_index=False).first()

# Remove the index that groupby may have set
df_unique.reset_index(drop=True, inplace=True)
print(df_unique)

Output:

   B  A  C
0  5  1  9
1  6  2  0
2  7  3  1

After grouping by column ‘B’, the first() aggregate function is used to keep the first row from each group. This ensures that only the first occurrence is retained. After that, reset_index is used to arrange the DataFrame back to its original form without a group index.

Method 3: Using a For-Loop and a Set

This manual method involves iterating over the DataFrame using a for-loop and a set to track seen elements. This is a more Pythonic approach, requiring no additional libraries, and it’s very flexible.

Here’s an example:

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3, 2],
    'B': [5, 6, 7, 6],
    'C': [9, 0, 1, 0]  
})

seen = set()
rows_to_drop = []

# Iterate through each row to determine duplicates
for index, row in df.iterrows():
    if row['B'] in seen:
        rows_to_drop.append(index)
    else:
        seen.add(row['B'])

# Drop the rows that were marked as duplicates
df_unique = df.drop(rows_to_drop)
print(df_unique)

Output:

   A  B  C
0  1  5  9
1  2  6  0
2  3  7  1

In this code, we create a set to store the values we’ve seen already in column ‘B’. As we iterate through the DataFrame, we check if an element is in the set. If it is, it means it’s a duplicate, and we mark that index for removal. Otherwise, we add it to the set. Once we’ve identified all duplicates, we drop them from the DataFrame.

Method 4: Using Boolean Indexing

Boolean indexing in Pandas allows us to select rows based on the truth value of an expression. It can be used to filter out rows that have been seen earlier in the DataFrame.

Here’s an example:

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3, 2],
    'B': [5, 6, 7, 6],
    'C': [9, 0, 1, 0]  
})

# Create a boolean series to flag the duplicates
duplicates = df.duplicated(subset='B', keep='first')

# Invert the boolean series to filter out duplicates
df_unique = df[~duplicates]
print(df_unique)

Output:

   A  B  C
0  1  5  9
1  2  6  0
2  3  7  1

The boolean series duplicates flags all duplicates as True except for the first occurrence. By negating this series with ~, we create a filter that selects only the rows that are not marked as duplicates and use it to derive the df_unique DataFrame.

Bonus One-Liner Method 5: Using drop_duplicates in a Single Line of Code

You can streamline the process of removing duplicates by using a single line of code. This method is quick, effective, and easy to read, perfect for minimalists.

Here’s an example:

import pandas as pd

# Original DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 2], 'B': [5, 6, 7, 6], 'C': [9, 0, 1, 0]})

# Remove duplicates with a one-liner
df_unique = df[~df.duplicated('B')]
print(df_unique)

Output:

   A  B  C
0  1  5  9
1  2  6  0
2  3  7  1

This one-liner code instantiates a Pandas DataFrame and uses the ~ operator to select all rows that are not duplicates based on column ‘B’. As a result, the output displays a DataFrame with unique values in the specified column.

Summary/Discussion

  • Method 1: Pandas drop_duplicates. Simple and powerful. May not perform optimally with extremely large datasets.
  • Method 2: GroupBy followed by drop. Versatile and easily understandable. Grouping can be more resource-intensive than other methods.
  • Method 3: For-Loop with a Set. No third-party libraries needed. Can be slow for large datasets due to the explicit Python loop.
  • Method 4: Boolean Indexing. Expressive and concise. The creation of extra Series might not be memory efficient for larger datasets.
  • One-Liner Method 5: Drop_duplicates One-Liner. Extremely concise. However, limited to only one kind of operation and lacks the detailed control of other methods.