💡 Problem Formulation: When working with datasets in Python Pandas, it’s common to encounter duplicate entries. Sometimes, it’s necessary to create a subset of this data, ensuring that for each set of duplicates only the last entry is kept. Suppose you have a DataFrame where the ‘id’ column has duplicates. The goal is to retain only the last occurrence of each ‘id’ while discarding the rest, resulting in a clean subset of your original data.
Method 1: Using drop_duplicates() Function
The drop_duplicates() function in Pandas is instrumental in removing duplicate rows from a DataFrame. By default, it keeps the first occurrence and removes subsequent duplicates, but you can modify its behavior using the keep parameter. Setting keep='last' keeps only the last occurrence of each duplicate.
Here’s an example:
import pandas as pd
# Sample DataFrame with duplicates
df = pd.DataFrame({
'id': [1, 2, 2, 3],
'value': ["apple", "banana", "banana", "cranberry"]
})
# Subset with the last entry from duplicates
subset_df = df.drop_duplicates(subset='id', keep='last')
# Display the subset
print(subset_df)Output:
id value 1 2 banana 3 3 cranberry
This code creates a DataFrame and then uses the drop_duplicates() method, specifying the column to consider for duplicates, and setting keep='last' to ensure only the last entry is retained. The result is a DataFrame that excludes the earlier duplicate values based on the ‘id’ column.
Method 2: Sorting and Using drop_duplicates()
Another approach involves sorting your DataFrame before using the drop_duplicates() function. This method ensures that you have control over which entries are considered ‘last’—you can sort by a column that dictates the importance of each row.
Here’s an example:
import pandas as pd
# Sample DataFrame with duplicates
df = pd.DataFrame({
'id': [1, 1, 2, 3, 3],
'timestamp': pd.to_datetime(['2021-01-01', '2021-02-01', '2021-01-15', '2021-01-20', '2021-02-20']),
'value': ["apple", "apple", "banana", "cranberry", "cranberry"]
})
# Sort by timestamp and drop duplicates
df_sorted = df.sort_values('timestamp', ascending=True)
subset_df = df_sorted.drop_duplicates(subset='id', keep='last')
# Display the subset
print(subset_df)Output:
id timestamp value 4 3 2021-02-20 cranberry 1 1 2021-02-01 apple 2 2 2021-01-15 banana
Here, we first sort the DataFrame using the sort_values() method by ‘timestamp’ to define which entry is considered last. Then, the drop_duplicates() method is used, similar to Method 1. As a result, the DataFrame’s subset retains the last entry by the timestamp for each ‘id’.
Method 3: Using groupby() and last()
The groupby() method in Pandas allows grouping data according to a certain criterion, and the last() function within this context provides the last row of each group. This method comes in use when we want to apply this operation for more complex subsets or criteria.
Here’s an example:
import pandas as pd
# Sample DataFrame with duplicates
df = pd.DataFrame({
'id': [1, 1, 2, 2, 3],
'value': ["apple", "apple", "banana", "banana", "cranberry"]
})
# Group by 'id' and get the last entry of each group
last_entries = df.groupby('id').last().reset_index()
# Display the last entries
print(last_entries)Output:
id value 0 1 apple 1 2 banana 2 3 cranberry
This snippet groups the DataFrame by the ‘id’ column and uses the last() function to obtain the last entry from each group. It effectively removes duplicate ‘id’ values while retaining the last occurrence in the DataFrame.
Method 4: Using Boolean Indexing with duplicated()
Boolean indexing can be paired with the duplicated() method, which marks duplicates as ‘True’ and the rest as ‘False’. By inverting the Boolean values using the tilde (~), we can select non-duplicate rows, while specifying keep='last' marks all but the last duplicates.
Here’s an example:
import pandas as pd
# Sample DataFrame with duplicates
df = pd.DataFrame({
'id': [1, 1, 2, 3, 3],
'value': ["apple", "apple", "banana", "cranberry", "cranberry"]
})
# Use Boolean indexing with duplicated()
subset_df = df[~df.duplicated(subset='id', keep='last')]
# Display the subset
print(subset_df)Output:
id value 1 1 apple 2 2 banana 4 3 cranberry
By using the duplicated() method with `keep=’last’`, the code snippet marks all duplicates except for the last occurrence as “True”. Boolean indexing with the inversion operator ~ selects only the rows marked as “False”, which are the last entries for each ‘id’.
Bonus One-Liner Method 5: Using groupby() with a Lambda Function
This method provides a one-liner alternative to retrieve the last entries of each group using groupby() and a lambda function that selects the last row.
Here’s an example:
import pandas as pd
# Sample DataFrame with duplicates
df = pd.DataFrame({
'id': [1, 1, 2, 3, 3],
'value': ["apple", "apple", "banana", "cranberry", "cranberry"]
})
# One-liner to get the last entry of each group
subset_df = df.groupby('id', as_index=False).apply(lambda x: x.iloc[-1])
# Display the subset
print(subset_df)Output:
id value 0 1 apple 1 2 banana 2 3 cranberry
The lambda function is applied to each group created by groupby(), with x.iloc[-1] specifying the last row of each group. This is an elegant one-line solution to the problem.
Summary/Discussion
- Method 1: Using
drop_duplicates()Function. Straightforward, ideal for simple de-duplication tasks. May not handle complex sorting prior to duplication removal. - Method 2: Sorting and Using
drop_duplicates(). Provides control over the order of entries considered. Requires sorting, which can be computationally expensive on large datasets. - Method 3: Using
groupby()andlast(). Suits complex grouping needs, but may be overkill for simple tasks and can be slower than other methods for large datasets. - Method 4: Using Boolean Indexing with
duplicated(). Flexible and powerful for complex filtering, but can be less readable due to the inversion operation~. - Bonus Method 5: Using
groupby()with a Lambda Function. Compact and elegant, but may be difficult for beginners to understand and debug.