帶有來自另一個DataFrame的條件的Pandas groupby（）

Question

我正在嘗試使用trial2中的信息創建新列trial1 ['Return']。 我需要在trial1中的給定時間范圍內獲得特定ID的收益乘積。

我嘗試將lambda與groupby（）一起使用，以及僅進行條件優化。 但是兩者都導致錯誤。 唯一有效的方法是for循環。 但是我想知道這樣做是否更有效。

import pandas as pd

trial1 = pd.DataFrame([[1,'2016-09-01','2016-09-05'],[1,'2016-09-03','2016-09-06'],[2,'2016-09-01','2016-09-05']] , columns=('Id','startDate','EndDate'))
trial1

trial2 = pd.DataFrame([[1,'2016-09-01',1.1],[1,'2016-09-02',1],[1,'2016-09-03',1],[1,'2016-09-04',1],[1,'2016-09-05',1],[1,'2016-09-06',1],[2,'2016-09-01',1.2],[2,'2016-09-02',1],[2,'2016-09-03',1],[2,'2016-09-04',1],[2,'2016-09-05',1]] , columns=('Id','Date','Return'))
trial2

trial1['EndDate'] = pd.to_datetime(trial1['EndDate'])
trial1['startDate'] = pd.to_datetime(trial1['startDate'])
trial2['Date'] = pd.to_datetime(trial2['Date'])

##This throws a Timestamp error
trial2_g = trial2.groupby('Id')
trial2_g.apply(lambda x: x[x['Date'].isin(pd.date_range(trial1['startDate'], trial1['EndDate']))]['Return'].prod())

##This throws a ValueError (can only compare identical-labeled series object)
trial2['Id'] = trial2['Id'].reset_index(drop=True)
trial1['Id'] = trial1['Id'].reset_index(drop=True)

trial1['Return'] = trial2[((trial2['Id']==trial1['Id']))
                    &(trial2['Date'].isin(pd.date_range(trial1['startDate'],trial1['EndDate'])))].prod()


##THIS WORKS AND THAT'S HOW I WANT IT TO LOOK LIKE
trial1['Return'] = 0
for nn in range(len(trial1)):
     trial1['Return'].loc[nn] = trial2.Return[(trial2.Id == trial1.Id[nn])
                  &(trial2.Date >= trial1.startDate[nn])
                  &(trial2.Date <= trial1.EndDate[nn])].prod()
trial1

Answer 1

我首先將索引設置為trial2

t2 = trial2.set_index(['Id', 'Date'])

然后在trial1上使用apply

trial1['Return'] = trial1.apply(
    lambda x: t2.xs(x.Id)[x.startDate:x.EndDate].prod(), 1)
trial1