根据 pandas 中的给定条件派生特征或列

我有 df 如下所示

ID     Age_days    N_30     N_31_90     N_91_180      N_180_365
1      201         60       15          30            1
2      800         0        15          5             10
3      800         0        0           10            6
4      100         0        0           0             370
5      600         0        6           5             10
6      800         0        0           15            6
7      500         10       10          30            9
8      200         0        0           0             0
9      500         0        0           0             0

从上面df我想派生一个名为Recency

解释：

if df['N_30'] != 0, then Recency = (30/df['N_30'])
elif df['N_31_90'] != 0 then Recency = 30 + (60/df['N_31_90'])
elif df['N_91_180'] != 0 then Recency = 90 + (90/df['N_91_180'])
elif df['N_181_365'] != 0 then Recency = 180 + (185/df['N_181_365'])
else 
  if df['age_days'] <= 365, Recency = df['age_days']
  else Recency = 366

预期输出：

ID     Limit    N_30     N_31_90     N_91_180      N_180_365    Recency
1      201      60       15          30            1            (30/60) = 0.5
2      800      0        15          5             10           30+(60/15) = 34
3      800      0        0           10            6            90+90/10 = 100
4      100      0        0           0             370          180+(185/370) = 180.5           
5      600      0        6           5             10           30+(60/6) = 36
6      800      0        0           15            6            90+(90/15) = 96
7      500      10       10          30            9            30/10 = 3
8      200      0        0           0             0            200
9      500      0        0           0             0            366

我尝试了下面的代码

pd.set_option("use_inf_as_na", True)
df2 = df[['N_30', 'N_31_90', 'N_91_180', 'N_180_365']]
df["Recency"] = (df2.eq(0) * [30, 60, 90, 180]).sum(1) + ([30, 60, 90, 185] / df2).bfill(1).iloc[:, 0]
df["Recency"].fillna(366)

Use numpy.select https://numpy.org/doc/stable/reference/generated/numpy.select.html

import numpy as np

conditions = [df['N_30'] != 0, df['N_31_90'] != 0, df['N_91_180'] != 0, df['N_180_365'] != 0, df['Age_days'] <= 365]

choices = [(30/df['N_30']), 30 + (60/df['N_31_90']), 90 + (90/df['N_91_180']), 180 + (185/df['N_180_365']), df['Age_days']]

df['Recency']=np.select(conditions, choices, default=366)

Output:

   ID  Age_days  N_30  N_31_90  N_91_180  N_180_365  Recency
0   1       201    60       15        30          1      0.5
1   2       800     0       15         5         10     34.0
2   3       800     0        0        10          6     99.0
3   4       100     0        0         0        370    180.5
4   5       600     0        6         5         10     40.0
5   6       800     0        0        15          6     96.0
6   7       500    10       10        30          9      3.0
7   8       200     0        0         0          0    200.0
8   9       500     0        0         0          0    366.0

我假设很少有修正，我正在使用N_180_365代替N_181_365您有条件但没有 DF。