How to Remove Duplicate Rows in a DataFrame While Prioritizing Maximum Values in a Specific Column?

Removing Duplicate Rows While Prioritizing Maximum Values in Column B

Dealing with duplicate rows in a DataFrame can often pose challenges. In this case, the objective is to eliminate duplicate rows based on the values in column A and retain the row with the highest value in column B.

To achieve this, a combination of operations can be applied. Firstly, the DataFrame can be sorted by column B in descending order using the sort_values function. This arranges the rows with the highest values for column B at the top.

df = df.sort_values('B', ascending=False)

Next, the drop_duplicates function can be employed to remove duplicate rows based on the values in column A. However, to maintain the prioritized rows, the keep parameter is set to last. This ensures that the row with the latest occurrence (typically the row with the highest value in column B) is retained.

df = df.drop_duplicates(subset='A', keep='last')

Alternatively, the groupby function combined with apply can be leveraged to accomplish the task. This approach groups the DataFrame by column A and applies a lambda function to each group. Within the lambda function, the idxmax method is used to identify the index of the row with the maximum value for column B. The resulting DataFrame contains only the rows assigned to those maximum values.

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

Applying these methods achieves the desired outcome of removing duplicate rows based on column A while preserving the rows with the highest values in column B.

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