在不同条件下向 pandas Dataframe 添加列

我有一个熊猫数据框。

import pandas as pd
data = pd.DataFrame({
'a': [0,1,0,0,1,1,0,1], 
'b': [0,0,1,0,1,0,1,1], 
'c': [0,0,0,1,0,1,1,1],
'rate': [0,0.1,0.11,0.12,0.24,0.27,0.3,0.4]})

a、b、c 是我的通道，我添加另一列，通过编写以下内容来显示这些通道的行总计总和：

data['total'] = data.a + data.b + data.c

data

    a   b   c   rate    total
1   1   0   0   0.10    1
2   0   1   0   0.11    1 
3   0   0   1   0.12    1
4   1   1   0   0.24    2
5   1   0   1   0.27    2
6   0   1   1   0.30    2
7   1   1   1   0.40    3

我想处理总计 = 1 且总计 = 2 的数据

reduced = data[(data.a == 1) & (data.total == 2)]
print(reduced)

     a  b  c  rate  total
  4  1  1  0  0.24      2
  5  1  0  1  0.27      2

我想将列添加到这个简化的数据框中，如下所示：

      a  b  c  rate  total  prob_a  prob_b  prob_c
   4  1  1  0  0.24      2     0.1    0.11    0
   5  1  0  1  0.27      2     0.1     0      0.12

在简化数据帧的第一行中，由于 C 不存在 ( ABC => 110)，所以 prob_c 为 0。在简化数据帧的第二行中，由于 B 不存在，所以 prob_b 为 0 (ABC => 101)

where,

# Channel a alone occurs (ABC => 100)
prob_a = data['rate'][(data.a == 1) & (data.total == 1)]

# Channel b alone occurs (ABC => 010)
prob_b = data['rate'][(data.b == 1) & (data.total == 1)]

# Channel c alone occurs (ABC => 001)
prob_c = data['rate'][(data.c == 1) & (data.total == 1)]

我试过这个：

reduced['prob_a'] = data['rate'][(data.a == 1) & (data.total == 1)]
reduced['prob_b'] = data['rate'][(data.b == 1) & (data.total == 1)]
reduced['prob_c'] = data['rate'][(data.c == 1) & (data.total == 1)]
print(reduced)

这会产生以下输出：

   a  b  c  rate  total  prob_a  prob_b  prob_c
4  1  1  0  0.24      2     NaN     NaN     NaN
5  1  0  1  0.27      2     NaN     NaN     NaN

IIUC

reduced['prob_a'] = data.loc[(data.a == 1) & (data.total == 1),'rate'].values[0]
reduced['prob_b'] = data.loc[(data.b == 1) & (data.total == 1),'rate'].values[0]
reduced['prob_c'] = data.loc[(data.c == 1) & (data.total == 1),'rate'].values[0]

reduced[['prob_a','prob_b','prob_c']]=reduced[['prob_a','prob_b','prob_c']].mul(reduced[['a','b','c']].eq(1).values)
reduced
Out[698]: 
   a  b  c  rate  total  prob_a  prob_b  prob_c
4  1  1  0  0.24      2     0.1    0.11    0.00
5  1  0  1  0.27      2     0.1    0.00    0.12