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

You can use apply instead of agg to construct all the columns in one go.

result = (
    df.groupby('Group').apply(lambda x: [np.max(x.Score),
                              df.loc[x.Score.idxmax(),'Element'],
                              df.loc[x.Score.idxmax(),'Case'],
                              np.min(x.Evaluation)])
      .apply(pd.Series)
      .rename(columns={0:'Max_score_value',
                       1:'Max_score_element',
                       2:'Max_score_case',
                       3:'Min_evaluation'})
      .reset_index()
)



result
Out[9]: 
  Group  Max_score_value  Max_score_element Max_score_case  Min_evaluation
0     A             9.19                  1              y            0.41
1     B             9.12                  2              x            0.10