How to Remove Duplicate Indexed Rows in Pandas?

Removing Duplicate Indexed Rows in Pandas

In pandas, duplicate index values can arise in various scenarios, such as when appending data from multiple sources or correcting erroneous observations. Removing these duplicate rows is essential for data consistency and analysis accuracy.

One recommended approach is utilizing the ~df3.index.duplicated(keep='first') method. This method efficiently identifies and drops duplicate rows while preserving the unique rows in the dataframe:

df3 = df3[~df3.index.duplicated(keep='first')]

This method outperforms other techniques, such as drop_duplicates and groupby, in terms of performance, especially for large dataframes. Additionally, it is more readable and easy to comprehend.

For MultiIndex dataframes, the ~df1.index.duplicated(keep='last') method can be employed, which retains the last occurrence of each unique index value:

df1[~df1.index.duplicated(keep='last')]

Using this approach ensures that the resulting dataframe contains only unique index values, eliminating redundant rows that can interfere with data analysis and modeling.

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