按期间分组的滚动总和

Question

我有这个 dataframe：

lst=[['01012021','A',10],['01012021','B',20],['02012021','A',12],['02012021','B',23]]
df2=pd.DataFrame(lst,columns=['Date','FN','AuM'])

我想按日期和 FN 获得滚动总和。 期望的结果如下所示：

lst=[['01012021','A',10,''],['01012021','B',20,''],['02012021','A',12,22],['02012021','B',23,33]]
df2=pd.DataFrame(lst,columns=['Date','FN','AuM','Roll2PeriodSum'])

你能帮帮我吗？

谢谢

Answer 1

解决方案如果连续的日期时间，不使用列date来计算每组：

df2['Roll2PeriodSum'] = (df2.groupby('FN').AuM
                            .rolling(2)
                            .sum() 
                            .reset_index(level=0, drop=True))
print (df2)
       Date FN  AuM  Roll2PeriodSum
0  01012021  A   10             NaN
1  01012021  B   20             NaN
2  02012021  A   12            22.0
3  02012021  B   23            43.0

使用日期时间的解决方案，使用列date进行计数：

df2['Date'] = pd.to_datetime(df2['Date'], format='%d%m%Y')

df = (df2.join(df2.set_index('Date')
                  .groupby('FN').AuM
                  .rolling('2D')
                  .sum().rename('Roll2PeriodSum'), on=['FN','Date']))
print (df)
        Date FN  AuM  Roll2PeriodSum
0 2021-01-01  A   10            10.0
1 2021-01-01  B   20            20.0
2 2021-01-02  A   12            22.0
3 2021-01-02  B   23            43.0

---

df = (df2.join(df2.set_index('Date')
                  .groupby('FN').AuM
                  .rolling('2D', min_periods=2)
                  .sum()
                  .rename('Roll2PeriodSum'), on=['FN','Date']))
print (df)
        Date FN  AuM  Roll2PeriodSum
0 2021-01-01  A   10             NaN
1 2021-01-01  B   20             NaN
2 2021-01-02  A   12            22.0
3 2021-01-02  B   23            43.0

Answer 2

使用groupby.rolling.sum ：

df2['Roll2PeriodSum'] = (
    df2.assign(Date=pd.to_datetime(df2['Date'], format='%d%m%Y'))
       .groupby('FN').rolling(2)['AuM'].sum().droplevel(0)
)
print(df2)

# Output
       Date FN  AuM  Roll2PeriodSum
0  01012021  A   10             NaN
1  01012021  B   20             NaN
2  02012021  A   12            22.0
3  02012021  B   23            43.0