Mumbojumbo .rolling() .max() .groupby() combination in python pandas-CodePudding

I am looking to do a "rolling" .max() .min() of B column "groupedby" date(column A values). However, trick is it should start on every row again so i can not use for example anything like df['MAX'] = df['B'].rolling(10).max().shift(-9) (couse i need to end it where group ends - every group can have different number of rows) or simply groupby A column (becouse i need that rolling max min with start on each row and end where each group ends - which means for row 1 column C is max of rows 1-4 in column B, for row 2 column C is max of rows 2-4 from column B, for row 3 column C is max of rows 3-4 from column B, for row 4 column C is max of row 4 from column B etc etc..). Hope it make sence - columns C and D are desired results. Thank you all in advance.

     A              B        C(max)   D(min)
1   2016-01-01      0        7        0     
2   2016-01-01      7        7        3
3   2016-01-01      3        4        3
4   2016-01-01      4        4        4  
5   2016-01-02      2        5        1
6   2016-01-02      5        5        1
7   2016-01-02      1        1        1         
8   2016-01-03      1        4        1
9   2016-01-03      3        4        2
10  2016-01-03      4        4        2
11  2016-01-03      2        2        2

CodePudding user response：

df['C_max'] = df.groupby('A')['B'].transform(lambda x: x[::-1].cummax()[::-1])
df['D_min'] = df.groupby('A')['B'].transform(lambda x: x[::-1].cummin()[::-1])

            A  B  C(max)  D(min)  C_max  D_min
1   2016-01-01  0       7       0      7      0
2   2016-01-01  7       7       3      7      3
3   2016-01-01  3       4       3      4      3
4   2016-01-01  4       4       4      4      4
5   2016-01-02  2       5       1      5      1
6   2016-01-02  5       5       1      5      1
7   2016-01-02  1       1       1      1      1
8   2016-01-03  1       4       1      4      1
9   2016-01-03  3       4       2      4      2
10  2016-01-03  4       4       2      4      2
11  2016-01-03  2       2       2      2      2