How to Eliminate Duplicate Rows in a DataFrame, Keeping Only the Rows with the Highest Values in a Specific Column?

How to Eliminate Duplicates by Columns, Retaining Rows with the Highest Values

When confronted with duplicate values in one column of a DataFrame, it becomes necessary to implement strategies to eliminate them. One approach is to preserve only the rows with the highest values in another column.

Consider this example DataFrame:

A B
1 10
1 20
2 30
2 40
3 10

The goal is to transform this DataFrame into:

A B
1 20
2 40
3 10

One method involves sorting the DataFrame before eliminating duplicates:

df.sort_values(by='B', ascending=False).drop_duplicates(subset='A')

However, for more complex scenarios involving multiple columns and nuanced sorting requirements, groupby can be employed. The code below demonstrates this approach:

df.groupby('A', group_keys=False).apply(lambda x: x.loc[x.B.idxmax()])

This solution groups the rows by column 'A' and yields the row with the maximum value in column 'B' for each group.

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