Handling Duplicates in Pandas: Retain Last Occurrences and Get Unique Indices

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💡 Problem Formulation: When working with datasets in Pandas, one often encounters the need to identify unique indices after removing duplicate values, while keeping the index of the last occurrence of each value. For example, given a dataset with duplicate ‘IDs’ where each ID should be unique, the challenge is to remove duplicates but retain the row of the last occurrence for each ID. The desired output is a Pandas Series or Index object containing the indices of these last occurrences.

Method 1: Use DataFrame.duplicated() with keep=’last’

This method involves using DataFrame.duplicated() to create a boolean mask that tags duplicates with False, except for the last occurrence. By setting the keep parameter to ‘last’, we ensure that the mask reflects the last occurrence as unique.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'ID': [101, 102, 103, 101, 104, 102],
    'Value': ['A', 'B', 'C', 'A', 'D', 'B']
})

# Find indices of the last occurrence of duplicated 'ID'
indices = df.index[~df.duplicated(subset='ID', keep='last')]

print(indices)

Output:

Int64Index([2, 3, 4, 5], dtype='int64')

This example demonstrates how to identify the unique indices of the last occurrences of duplicate values in a ‘ID’ column. The inverted boolean mask ~df.duplicated(subset='ID', keep='last') is used to filter the DataFrame index, resulting in the indices of non-duplicate entries.

Method 2: Using DataFrame.drop_duplicates() with keep=’last’

The method employs DataFrame.drop_duplicates(), specifying the keep argument as ‘last’, to drop duplicate rows while retaining the final occurrence. It then extracts the indices of the remaining rows, which are unique by definition.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'ID': [101, 102, 103, 101, 104, 102],
    'Value': ['A', 'B', 'C', 'A', 'D', 'B']
})

# Dropping duplicates and keeping the last occurrences
unique_df = df.drop_duplicates(subset='ID', keep='last')

# Get the unique indices
unique_indices = unique_df.index

print(unique_indices)

Output:

Int64Index([2, 3, 4, 5], dtype='int64')

In this example, we drop duplicates using df.drop_duplicates(subset='ID', keep='last'), which returns a DataFrame with unique rows based on the ‘ID’ column, keeping only the last occurrence. Extracting the index attribute gives us the desired unique indices.

Method 3: Group by ID and Aggregate with idxmax()

Another approach is to use groupby() on the ‘ID’ column and then apply the idxmax() function on a column that naturally sorts last occurrences to the back, such as an index or timestamp. This method returns the index of the last occurrence within each group.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'ID': [101, 102, 103, 101, 104, 102],
    'Value': ['A', 'B', 'C', 'A', 'D', 'B']
})

# Group by 'ID' and use 'idxmax' to find the index of the last occurrence
indices = df.groupby('ID').apply(lambda x: x.index.max())

print(indices)

Output:

ID
101    3
102    5
103    2
104    4
dtype: int64

By grouping the DataFrame by ‘ID’ and applying lambda x: x.index.max(), this code determines the maximum index within each group, effectively giving us the index of the last occurrence of each ID.

Method 4: Sort by ID and Index, then Use drop_duplicates with keep=’last’

In this method, we first sort the DataFrame by ‘ID’ and by the original index to ensure that the last occurrences are at the bottom. We then apply drop_duplicates() with keep='last' to guarantee that we retain these final occurrences.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'ID': [101, 102, 103, 101, 104, 102],
    'Value': ['A', 'B', 'C', 'A', 'D', 'B']
}).reset_index()

# Sort by 'ID' and 'index', then drop duplicates while keeping the last occurrence
sorted_df = df.sort_values(by=['ID', 'index'])
unique_df = sorted_df.drop_duplicates(subset='ID', keep='last')

# Get the unique indices
unique_indices = unique_df['index']

print(unique_indices)

Output:

2    2
3    3
5    5
4    4
Name: index, dtype: int64

After sorting the DataFrame by ‘ID’ and ‘index’, we drop duplicates with keep='last', which aids in retaining the last occurrences when extracting the ‘index’ column. This gives us a Pandas Series of unique indices.

Bonus One-Liner Method 5: Chain boolean indexing with drop_duplicates()

A one-liner variation uses boolean indexing to directly query the DataFrame’s index after chaining drop_duplicates() with keep='last'.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'ID': [101, 102, 103, 101, 104, 102],
    'Value': ['A', 'B', 'C', 'A', 'D', 'B']
})

# Chain drop_duplicates() with index
unique_indices = df.drop_duplicates(subset='ID', keep='last').index

print(unique_indices)

Output:

Int64Index([2, 3, 4, 5], dtype='int64')

This crisp one-liner chains the drop_duplicates() with the DataFrame index extraction. It’s a compact and efficient way to achieve the goal without sorting or grouping.

Summary/Discussion

  • Method 1: DataFrame.duplicated with keep=’last’. Straightforward and easily readable. May not be the most efficient due to boolean masking.
  • Method 2: DataFrame.drop_duplicates with keep=’last’. Simple and intuitive. Results in a new DataFrame, which may have overhead if the index is all that’s needed.
  • Method 3: Group by and idxmax. Utilizes the power of groupby for complex datasets. Can be less intuitive and slightly more complex than other methods.
  • Method 4: Sort and then drop_duplicates. Ensures accuracy when datasets have a natural order. Additional operation of sorting can be computationally costly with large datasets.
  • Method 5: One-liner chaining. Elegant and succinct. Especially useful for quick analysis or scripting but less descriptive for those learning the process.