How to Remove Duplicates by Columns and Retain Rows with Maximum Values?

Removing Duplicates by Columns and Retaining Rows with Maximum Value

Encountering duplicate values in dataframes can be challenging. In a scenario where it's crucial to keep the rows with the highest corresponding values, it becomes essential to employ effective techniques.

To address this issue, consider the following dataframe with duplicates in column A:

The objective is to remove duplicates from column A but preserve the rows with the maximum values in column B. Ideally, the result should look like this:

One approach is to sort the dataframe before removing duplicates:

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

This method works but doesn't guarantee retaining the maximum values since it sorts rows in ascending order. To overcome this limitation, we can use the following approach:

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

This operation groups the dataframe by column A, finds the index with the maximum value for column B, and selects the corresponding row. The result is an updated dataframe with duplicates removed and maximum values preserved.

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