如果当前 cumsum 大于特定值，则从下一个 cumsum 中减去 1 - pandas 或 numpy [英] subtract 1 from next cumsum if current cumsum more than a particular value - pandas or numpy

问题描述

我有一个如下所示的数据框

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

其中cumulative_no_show 是no_show 的累积总和.

where cumulative_no_show is the cumulative sum of no_show.

从上面我想根据以下条件创建一个名为 u_no_show 的新列.

From the above I would like to create a new column called u_no_show based on below condition.

只要cumulative_no_show >= 0.8，就从下一个cumulative_no_show 中减去1.等等.

Whenever cumulative_no_show >= 0.8, then subtract 1 from next cumulative_no_show. and so on.

预期输出:

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

推荐答案

我假设您希望每个会话都执行此操作.我不确定是否有矢量化解决方案，所以我会通过创建一个函数来迭代这些值并在需要时进行减法，然后使用 groupby.apply:

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

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