There is a Pandas dataframe: song performer chart_debut peak_position worst_position time_on_chart consecutive_weeks 261636 Stupid Cupid Connie Francis 1958-08-02 17 72 12 11.0 46383 Chantilly Lace Big Bopper 1958-08-02 6 40 18 17.0 46384 Chantilly Lace Big Bopper 1958-08-02 6 40 19 18.0 46385 Chantilly Lace Big Bopper 1958-08-02 6 40 20 19.0 46386 Chantilly Lace Big Bopper 1958-08-02 6 40 21 20.0 Pandas dataframe is available: song artist chart_debut peak_position worst_position time_on_chart consecutive_weeks 261636 Stupid Cupid Connie Francis 1958-08-02 17 72 12 11.0 46383 Chantilly Lace Big Bopper 1958-08-02 6 40 18 17.0 46384 Chantilly Lace Big Bopper 1958-08-02 6 40 19 18.0 46385 Chantilly Lace Big Bopper 1958-08-02 6 40 20 19.0 46386 Chantilly Lace Big Bopper 1958-08-02 6 40 21 20.0 Write a function that will group all rows by the performer column (so that no duplicates are left), and all corresponding (unique) values to that column are written through the "," sign into a new hits column.

def group_by_performer(df):
    # make a copy of df
    df_copy = df.copy()

    # sort by performer
    df_copy.sort_values(by='performer', inplace=True)

    # group by song and take the unique performers
    df_copy['hits'] = df_copy.groupby('song')['performer'].transform(lambda x: ','.join(x.unique()))

    # drop duplicates and reset index
    df_copy.drop_duplicates(subset='performer', inplace=True)
    df_copy.reset_index(inplace=True)

    return df_copy
    
group_by_performer(df)