根据数据框中的其他列在具有多个条件的熊猫中添加列

Question

首先，如果这已经在 StackOverflow 上的某个地方，我深表歉意，我在自己试验了一个小时后搜索了一个小时，但找不到它。我确信一定有一个优雅的（可能是基本的）解决方案。

我有以下数据框：

    Admit   Gender  Dept    Freq
0   Admitted    Male    A   512
1   Rejected    Male    A   313
2   Admitted    Female  A   89
3   Rejected    Female  A   19
4   Admitted    Male    B   353
5   Rejected    Male    B   207
6   Admitted    Female  B   17
7   Rejected    Female  B   8
8   Admitted    Male    C   120
9   Rejected    Male    C   205
10  Admitted    Female  C   202
11  Rejected    Female  C   391
12  Admitted    Male    D   138
13  Rejected    Male    D   279
14  Admitted    Female  D   131
15  Rejected    Female  D   244
16  Admitted    Male    E   53
17  Rejected    Male    E   138
18  Admitted    Female  E   94
19  Rejected    Female  E   299
20  Admitted    Male    F   22
21  Rejected    Male    F   351
22  Admitted    Female  F   24
23  Rejected    Female  F   317

我想添加一个“比例”列，它给出了每个部门按性别划分的成功/失败申请人的比例。

这样：

df.loc[0, 'Proportion'] = 512/(512+313) = 0.6206
df.loc[1, 'Proportion'] = 313/(512+313) = 0.3794
...

等等。

我尝试通过使用以下变体添加“总计”列开始：

data.groupby(['Dept', 'Gender'])[['Freq']].sum()

但我似乎无法通过原始数据帧的每一行中的值来查找此数据帧的值。

我也尝试过使用 lambda 函数，但出现“函数不可迭代”错误。

我想人们可以逐行循环它，因为它是一个小数据集，但将来当我需要做这样的事情时，这将不是一个选择。

请帮助一位新手和有抱负的数据科学家。

Answer 1

对于与原始DataFrame 大小相同的系列，您可以将列除以div 和transform：

data['new'] = data['Freq'].div(data.groupby(['Dept', 'Gender'])['Freq'].transform('sum'))

或者使用 apply 和自定义函数：

data['new'] = data.groupby(['Dept', 'Gender'])['Freq'].apply(lambda x: x/x.sum())

---

print (data)
       Admit  Gender Dept  Freq       new
0   Admitted    Male    A   512  0.620606
1   Rejected    Male    A   313  0.379394
2   Admitted  Female    A    89  0.824074
3   Rejected  Female    A    19  0.175926
4   Admitted    Male    B   353  0.630357
5   Rejected    Male    B   207  0.369643
6   Admitted  Female    B    17  0.680000
7   Rejected  Female    B     8  0.320000
8   Admitted    Male    C   120  0.369231
9   Rejected    Male    C   205  0.630769
10  Admitted  Female    C   202  0.340641
11  Rejected  Female    C   391  0.659359
12  Admitted    Male    D   138  0.330935
13  Rejected    Male    D   279  0.669065
14  Admitted  Female    D   131  0.349333
15  Rejected  Female    D   244  0.650667
16  Admitted    Male    E    53  0.277487
17  Rejected    Male    E   138  0.722513
18  Admitted  Female    E    94  0.239186
19  Rejected  Female    E   299  0.760814
20  Admitted    Male    F    22  0.058981
21  Rejected    Male    F   351  0.941019
22  Admitted  Female    F    24  0.070381
23  Rejected  Female    F   317  0.929619