Python 3.x 基于2个变量约束计算数据帧中的滚动和

我想创建一个变量：sumofPreious5OccurnceSatidlevel，它是Var1在ID级别（第1列）的前5个值（根据日期变量）的总和，否则它将取NA值

样本数据和输出：

ID  Date      Var1  SumOfPrevious5OccurencesAtIDLevel
1   1/1/2018    0   NA
1   1/2/2018    1   NA
1   1/3/2018    2   NA
1   1/4/2018    3   NA
2   1/1/2018    4   NA
2   1/2/2018    5   NA
2   1/3/2018    6   NA
2   1/4/2018    7   NA
2   1/5/2018    8   NA
2   1/6/2018    9   30
2   1/7/2018    10  35
2   1/8/2018    11  40

与和功能一起使用，以及：

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如果数据没有按ID和日期排序，那么？df['new']=df.sort_values（['ID'，'Date']）.groupby（'ID'）['Var1']）.transform（lambda x:x.rolling（5.sum（）.shift（））@user3643528-很好，然后需要将列转换为datetime，并按照编辑后的答案进行排序。

df['Date'] = pd.to_datetime(df['Date'], format='%m/%d/%Y')
#if not sorted ID with datetimes
df = df.sort_values(['ID','Date'])

df['new'] = df.groupby('ID')['Var1'].transform(lambda x: x.rolling(5).sum().shift())
print (df)
    ID       Date  Var1  SumOfPrevious5OccurencesAtIDLevel   new
0    1 2018-01-01     0                                NaN   NaN
1    1 2018-01-02     1                                NaN   NaN
2    1 2018-01-03     2                                NaN   NaN
3    1 2018-01-04     3                                NaN   NaN
4    2 2018-01-01     4                                NaN   NaN
5    2 2018-01-02     5                                NaN   NaN
6    2 2018-01-03     6                                NaN   NaN
7    2 2018-01-04     7                                NaN   NaN
8    2 2018-01-05     8                                NaN   NaN
9    2 2018-01-06     9                               30.0  30.0
10   2 2018-01-07    10                               35.0  35.0
11   2 2018-01-08    11                               40.0  40.0