问题
I have a data frame as shown below.
B_ID No_Show Session slot_num Patient_count
1 0.4 S1 1 1
2 0.3 S1 2 1
3 0.8 S1 3 1
4 0.3 S1 3 2
5 0.6 S1 4 1
6 0.8 S1 5 1
7 0.9 S1 5 2
8 0.4 S1 5 3
9 0.6 S1 5 4
12 0.9 S2 1 1
13 0.5 S2 1 2
14 0.3 S2 2 1
15 0.7 S2 3 1
20 0.7 S2 4 1
16 0.6 S2 5 1
17 0.8 S2 5 2
19 0.3 S2 5 3
From the above I would like to find the cumulative No_show by Session
df['Cum_No_show'] = df.groupby(['Session'])['No_Show'].cumsum()
No we get
B_ID No_Show Session slot_num Patient_count Cumulative_No_show
1 0.4 S1 1 1 0.4
2 0.3 S1 2 1 0.7
3 0.8 S1 3 1 1.5
4 0.3 S1 3 2 1.8
5 0.6 S1 4 1 2.4
6 0.8 S1 5 1 3.2
7 0.9 S1 5 2 4.1
8 0.4 S1 5 3 4.5
9 0.6 S1 5 4 5.1
12 0.9 S2 1 1 0.9
13 0.5 S2 1 2 1.4
14 0.3 S2 2 1 1.7
15 0.7 S2 3 1 2.4
20 0.7 S2 4 1 3.1
16 0.6 S2 5 1 3.7
17 0.8 S2 5 2 4.5
19 0.3 S2 5 3 4.8
From the above I would like create a new column named as below
U_slot_num = Updated slot number
U_No_show = Updated cumulative no show
Whenever cumulative no show > 0.6 change the next slot_num as same as current one and increase patient count by one and update U_No_show as subtracting 1 as shown in expected output.
Expected output:
No_Show Session slot_num Patient_count Cum_No_show U_slot_num U_No_show
0.4 S1 1 1 0.4 1 0.4
0.3 S1 2 1 0.7 2 0.7
0.8 S1 3 1 1.5 2 0.5
0.3 S1 3 2 1.8 3 0.8
0.6 S1 4 1 2.4 3 0.4
0.8 S1 5 1 3.2 4 1.2
0.9 S1 5 2 4.1 4 0.2
0.4 S1 5 3 4.5 5 0.6
0.6 S1 5 4 5.1 6 1.2
0.9 S2 1 1 0.9 1 0.9
0.5 S2 1 2 1.4 1 0.4
0.3 S2 2 1 1.7 2 0.7
0.7 S2 3 1 2.4 2 0.4
0.7 S2 4 1 3.1 3 1.1
0.6 S2 5 1 3.7 3 0.7
0.8 S2 5 2 4.5 3 0.5
0.3 S2 5 3 4.8 4 0.8
来源：https://stackoverflow.com/questions/61346334/groupby-cumulative-in-pandas-then-update-using-numpy-based-specific-condition