按SQL等价后的行对分区进行求和

Question

我正在寻找一种基于特定分区（相当于

select table.*,
       sum(income) over (order by id, num_yyyymm rows between 3 preceding and 1 preceding) as prev_income_3,
       sum(income) over (order by id, num_yyyymm rows between 1 following and 3 following) as next_income_3
from table order by a.id_customer, num_yyyymm;

我尝试了以下解决方案，但存在一些问题： 1） 需要很长时间才能完成 2） 我必须在最后把所有结果合并起来

for x, y in df.groupby(['id_customer']):
    print(y[['num_yyyymm', 'income']])
    y['next3'] = y['income'].iloc[::-1].rolling(3).sum()
    print(y[['num_yyyymm', 'income', 'next3']])
    break

结果：

        num_yyyymm       income     next3
0       201501         0.00     0.00
1       201502         0.00     0.00
2       201503         0.00     0.00
3       201504         0.00     0.00
4       201505         0.00     0.00
5       201506         0.00     0.00
6       201507         0.00     0.00
7       201508         0.00     0.00
8       201509         0.00     0.00
9       201510         0.00     0.00
10      201511         0.00     0.00
11      201512         0.00     0.00
12      201601         0.00     0.00
13      201602         0.00     0.00
14      201603         0.00     0.00
15      201604         0.00     0.00
16      201605         0.00     0.00
17      201606         0.00     0.00
18      201607         0.00     0.00
19      201608         0.00     0.00
20      201609         0.00  1522.07
21      201610         0.00  1522.07
22      201611         0.00  1522.07
23      201612      1522.07     0.00
24      201701         0.00    -0.00
25      201702         0.00     1.52
26      201703         0.00  1522.07
27      201704         0.00  1522.07
28      201705         1.52  1520.55
29      201706      1520.55     0.00
30      201707         0.00      NaN
31      201708         0.00      NaN
32      201709         0.00      NaN

有人有别的解决办法吗？你知道吗