问题
I have a data frame as shown below
B_ID Session no_show cumulative_no_show
1 s1 0.4 0.4
2 s1 0.6 1.0
3 s1 0.2 1.2
4 s1 0.1 1.3
5 s1 0.4 1.7
6 s1 0.2 1.9
7 s1 0.3 2.2
10 s2 0.3 0.3
11 s2 0.4 0.7
12 s2 0.3 1.0
13 s2 0.6 1.6
14 s2 0.2 1.8
15 s2 0.5 2.3
where cumulative_no_show is the cumulative sum of no_show.
From the above I would like to create a new column called u_no_show based on below condition.
Whenever cumulative_no_show >= 0.8, then subtract 1 from next cumulative_no_show. and so on.
Expected Output:
B_ID Session no_show cumulative_no_show u_no_show
1 s1 0.4 0.4 0.4
2 s1 0.6 1.0 1.0
3 s1 0.2 1.2 0.2
4 s1 0.1 1.3 0.3
5 s1 0.4 1.7 0.7
6 s1 0.2 1.9 0.9
7 s1 0.3 2.2 0.2
10 s2 0.3 0.3 0.3
11 s2 0.4 0.7 0.7
12 s2 0.3 1.0 1.0
13 s2 0.6 1.6 0.6
14 s2 0.2 1.8 1.8
15 s2 0.5 2.3 0.3
回答1:
I assume you want to perform this per Session. I'm not sure there is a vectorized solution so I would go by creating a function that iterate over the values and do the subtraction when needed, then use groupby.apply:
def create_u_no_show (ser):
# convert to numpy aray and iterate
arr_ns = ser.to_numpy()
for i in range(len(arr_ns)-1):
# check if the condition is met
if arr_ns[i]>0.8:
# remove 1 to all the next values if the condition is met
arr_ns[i+1:] -= 1
# return a serie with the right index
return pd.Series(arr_ns, ser.index)
df['u_no_show'] = df.groupby(['Session'])['cumulative_no_show'].apply(create_u_no_show)
print (df)
B_ID Session no_show cumulative_no_show u_no_show
0 1 s1 0.4 0.4 0.4
1 2 s1 0.6 1.0 1.0
2 3 s1 0.2 1.2 0.2
3 4 s1 0.1 1.3 0.3
4 5 s1 0.4 1.7 0.7
5 6 s1 0.2 1.9 0.9
6 7 s1 0.3 2.2 0.2
7 10 s2 0.3 0.3 0.3
8 11 s2 0.4 0.7 0.7
9 12 s2 0.3 1.0 1.0
10 13 s2 0.6 1.6 0.6
11 14 s2 0.2 1.8 0.8
12 15 s2 0.5 2.3 1.3
来源：https://stackoverflow.com/questions/61364013/subtract-1-from-next-cumsum-if-current-cumsum-more-than-a-particular-value-pan