Filtering Duplicates by Column in Pandas (Highest Value Wins)

Scenario:

You have a DataFrame with duplicate rows based on certain columns (e.g., column A), and you want to keep only one row for each unique combination in those columns, but you want to ensure the row with the highest value in another column (e.g., column B) is retained.

Solution using pandas:

1. Import pandas:

   import pandas as pd
   

3. Sort the DataFrame by columns A and B (highest B value first):

   df = df.sort_values(by=['A', 'B'], ascending=False)
   

   • sort_values rearranges the DataFrame based on the specified columns.
   • by=['A', 'B'] defines which columns to use for sorting (A first, then B in descending order).
   • ascending=False ensures the rows with the highest B values appear first.

4. Drop duplicates, keeping only the first occurrence (the one with the highest B value):

   df_deduplicated = df.drop_duplicates(subset=['A'], keep='first')
   

   • drop_duplicates removes duplicate rows based on the specified columns.
   • subset=['A'] indicates that duplicates will be identified based on column A only.
   • keep='first' (the default behavior) retains the first occurrence of each unique combination in column A. Since you sorted by B first, this means the row with the highest B value is kept.

Explanation:

• Sorting by A and then B (descending) ensures that rows with the same A value but different B values have the highest B value first.
• Dropping duplicates using keep='first' on column A then removes subsequent rows with the same A value, keeping only the row with the highest B value (the one that appeared first after sorting).

Example:

import pandas as pd

data = {'A': ['X', 'X', 'Y', 'Y', 'Z'],
        'B': [10, 5, 20, 15, 30]}
df = pd.DataFrame(data)

df_deduplicated = df.sort_values(by=['A', 'B'], ascending=False).drop_duplicates(subset=['A'], keep='first')

print(df_deduplicated)

This will output:

   A   B
2  Y  20
4  Z  30

As you can see, duplicates in column A are removed, but the rows with the highest values in column B are preserved.

import pandas as pd

# Sample data with duplicates in 'A' and varying values in 'B'
data = {'A': ['X', 'X', 'Y', 'Y', 'Y', 'Z'],
        'B': [10, 5, 20, 15, 12, 30]}
df = pd.DataFrame(data)
print("Original DataFrame:")
print(df)

# Step 1: Sort by 'A' (primary) and 'B' (descending) to prioritize highest 'B' values for duplicates
df_sorted = df.sort_values(by=['A', 'B'], ascending=False)
print("\nSorted DataFrame (highest 'B' values first for duplicates in 'A'):")
print(df_sorted)

# Step 2: Drop duplicates based on 'A' (keep the first occurrence, which is the highest 'B' due to sorting)
df_deduplicated = df_sorted.drop_duplicates(subset=['A'], keep='first')
print("\nDataFrame with duplicates removed, keeping the row with the highest 'B' value for each 'A':")
print(df_deduplicated)

This code first creates a sample DataFrame with duplicate values in column 'A' and varying values in column 'B'. It then demonstrates the following steps:

The output shows the original DataFrame, the sorted DataFrame with highest 'B' values for duplicates, and finally the deduplicated DataFrame where only the row with the highest 'B' value remains for each unique 'A'.

Method 1: Using groupby and idxmax

1. Group the DataFrame by column A:

   grouped = df.groupby('A')
   

2. Find the index of the row with the maximum value in column B within each group:

   idx_max_B = grouped['B'].idxmax()
   

3. Select the rows with the maximum B value using boolean indexing:

   df_deduplicated = df.loc[idx_max_B]
   

   • loc allows selecting rows based on labels (indices in this case).

• groupby efficiently iterates through unique values in 'A'.
• We use boolean indexing with loc to select only the rows corresponding to the maximum 'B' values for each 'A'.

Method 2: Using nsmallest

1. df_sorted = df.sort_values(by=['A', 'B'], ascending=False)
   

2. Use nsmallest to select the top (smallest) row for each unique value in column A:

   df_deduplicated = df_sorted.nsmallest(1, 'A').drop_duplicates(subset='A', keep='all')
   

   • nsmallest(1, 'A') selects the smallest row (highest B value) for each unique value in 'A'.
   • drop_duplicates(subset='A', keep='all') removes remaining duplicates within each 'A' group (all rows after the first one).

• Similar to the first method, sorting ensures highest 'B' values appear first.
• nsmallest takes the smallest number of rows (1 in this case) for each unique value in 'A'.
• The final drop_duplicates removes any remaining duplicates within each 'A' group since only the highest 'B' row is left.

Choosing the Best Method:

• For small DataFrames, these methods might have similar performance.
• For larger DataFrames, groupby and idxmax might be slightly more efficient due to vectorized operations.

Experiment with different methods depending on your DataFrame size and preferences.