使用groupby和cumsum在pandas中获取新列

Question

我有以下数据框：

班级	已收到	已发布
FD	10	0
FD	0	2
RM	5	0
RM	0	3
FD	0	2
下午	5	0
下午	1	0
RM	1	0
FD	4	0

我需要下面的数据框：

班级	已收到	已发布	剩余数量
FD	10	0	10
FD	0	2	8
RM	5	0	5
RM	0	3	2
FD	0	2	6
下午	5	0	5
下午	1	0	6
RM	1	0	3
FD	4	0	10

剩余数量列是每个班级收到的-发出的cumsum()。我尝试过不同的方法，但我不明白。

Answer 1

df['Remaining Quantity'] = df.groupby('Class').apply(lambda x: x['Received'].cumsum() - x['Issued'].cumsum()).reset_index(level = 0, drop=True)

输出：

  Class  Received  Issued  Remaining Quantity
0    FD        10       0                  10
1    FD         0       2                   8
2    RM         5       0                   5
3    RM         0       3                   2
4    FD         0       2                   6
5    PM         5       0                   5
6    PM         1       0                   6
7    RM         1       0                   3
8    FD         4       0                  10

Answer 2

另一种可能的解决方案：

df["Remaining Quatity"] = (
    df.eval("tmp=Received-Issued").groupby("Class")["tmp"].cumsum()
)

输出：

print(df)

  Class  Received  Issued  Remaining Quatity
0    FD        10       0                 10
1    FD         0       2                  8
2    RM         5       0                  5
3    RM         0       3                  2
4    FD         0       2                  6
5    PM         5       0                  5
6    PM         1       0                  6
7    RM         1       0                  3
8    FD         4       0                 10

Answer 3

另一种解决方案：

df["Remaining Quatity"] = (g := df.groupby("Class").cumsum())["Received"] - g["Issued"]
print(df)

打印：

  Class  Received  Issued  Remaining Quatity
0    FD        10       0                 10
1    FD         0       2                  8
2    RM         5       0                  5
3    RM         0       3                  2
4    FD         0       2                  6
5    PM         5       0                  5
6    PM         1       0                  6
7    RM         1       0                  3
8    FD         4       0                 10

Answer 4

一种方法是使用

.stack

计算差异，然后沿索引将值分配回。

df['Remaining Quality'] = df.assign(
            Issued=df['Issued'] * -1).set_index('Class',append=True)\
           .stack().groupby(level=1).cumsum().unstack(-1).droplevel(1,0)['Issued']

print(df)

  Class  Received  Issued  Remaining Quality
0    FD        10       0                 10
1    FD         0       2                  8
2    RM         5       0                  5
3    RM         0       3                  2
4    FD         0       2                  6
5    PM         5       0                  5
6    PM         1       0                  6
7    RM         1       0                  3
8    FD         4       0                 10