Pandas: grouping and aggregation with multiple functions

Question

Situation

I have a pandas dataframe defined as follows:

import pandas as pd

headers = [\'Group\', \'Element\', \'Case\', \'Score\', \'Evaluation\'         


        

Answer 1

Here is possible solution with pd.merge

>> r = df.groupby('Group') \
>>       .agg({'Score': 'idxmax', 'Evaluation': 'min'}) \
>>       .rename(columns={'Score': 'idx'})
>> for c in ['Score', 'Element', 'Case']:
>>   r = pd.merge(r, df[[c]], how='left', left_on='idx', right_index=True)
>> r.drop('Score_idx', axis=1).rename(columns={'Score': 'Max_score_value', 
>>                                             'Element': 'Max_score_element', 
>>                                             'Case': 'Max_score_case'})
       Evaluation  Max_score_value  Max_score_element Max_score_case
Group                                                               
A            0.41             9.19                  1              y
B            0.10             9.12                  2              x

Though it provides the desired output, I am not sure about if it's not less efficient than yours approach.