我怎样才能在一个班轮代码中实现愿望 output？

Question

i have dataframe given below and i want to achieve the output in one liner code. my codes are also attached.

df : -totscrd_n_r
FRANK   SCORE
1       0.748180912
2       0.288977296
3       0.233826294
4       0.199272093
5       0.175346525
1       0.162129932
2       0.152657008
3       0.144826844
4       0.136572409
5       0.122732783
1       0.288984226
2       0.233826364
3       0.199273169
4       0.175346964
5       0.162130909
1       0.152657357
2       0.144827363
3       0.136572485
4       0.12273334
5       0.050275945

output : - TEMP3_10GRP
FRANK   HIGH        LOW
1       0.748180912 0.152657357
2       0.288977296 0.144827363
3       0.233826294 0.136572485
4       0.199272093 0.12273334
5       0.175346525 0.050275945

my code:- 
TEMP3_10GRPH = pd.DataFrame(totscrd_n_r.groupby(['FRANK'])['SCORE'].max().reset_index())
TEMP3_10GRPH.rename(columns = {'SCORE':'HIGH'}, inplace = True)
TEMP3_10GRPL = pd.DataFrame(totscrd_n_r.groupby(['FRANK'])['SCORE'].min().reset_index())
TEMP3_10GRPL.rename(columns = {'SCORE':'LOW'}, inplace = True)

TEMP3_10GRP = pd.merge(TEMP3_10GRPH, TEMP3_10GRPL, left_on='FRANK', right_on='FRANK', how = 'left')

TEMP3_10GRP

can i achieve this with a shorter way?

Thanks in advance

Answer 1

Groupby has a method .agg() or .aggregate() specially for this.

df.groupby().agg(['max', 'min'])

Answer 2

You can use .agg as @ RichieV pointed out,

>>> import pandas as pd
>>> ...
>>> df
    FRANK     SCORE
0       1  0.748181
1       2  0.288977
2       3  0.233826
3       4  0.199272
4       5  0.175347
5       1  0.162130
6       2  0.152657
7       3  0.144827
8       4  0.136572
9       5  0.122733
10      1  0.288984
11      2  0.233826
12      3  0.199273
13      4  0.175347
14      5  0.162131
15      1  0.152657
16      2  0.144827
17      3  0.136572
18      4  0.122733
19      5  0.050276
>>> df.groupby('FRANK').agg(High=pd.NamedAgg(column="SCORE", aggfunc="max"), Low=pd.NamedAgg(column="SCORE", aggfunc="min"))
           High       Low
FRANK                    
1      0.748181  0.152657
2      0.288977  0.144827
3      0.233826  0.136572
4      0.199272  0.122733
5      0.175347  0.050276
>>>