python - Pandas : Cumulative sum with moving window (following and preceding rows)

Question

我有以下数据集:

date     sales
201201   5
201202   5
201203   5
201204   5
201205   5
201206   5
201207   5
201208   5
201209   5
201210   5
201211   5
201212   5
201301   100
201302   100

我想计算从开始到实际日期 + 12 个月的累计销售额

所以在这里:

date     sales   expected
201201   5       60
201202   5       160
201203   5       260
201204   5       260 
201205   5       260
201206   5       260
201207   5       260
201208   5       260
201209   5       260
201210   5       260
201211   5       260
201212   5       260
201301   100     260
201302   100     260

根据这个问题How to compute cumulative sum of previous N rows in pandas?我尝试过:

df['sales'].rolling(window=12).sum()

但是我正在寻找更像这样的东西:

df['sales'].rolling(window=['unlimited preceding, 11 following']).sum()

Answer 1

使用cumsum直接感谢shift通过11 ，而不是使用ffill填写NaN s 与先前的值:

df['expected'] = df['sales'].cumsum().shift(-11).ffill()

现在:

print(df)

是:

      date  sales  expected
0   201201      5      60.0
1   201202      5     160.0
2   201203      5     260.0
3   201204      5     260.0
4   201205      5     260.0
5   201206      5     260.0
6   201207      5     260.0
7   201208      5     260.0
8   201209      5     260.0
9   201210      5     260.0
10  201211      5     260.0
11  201212      5     260.0
12  201301    100     260.0
13  201302    100     260.0