python - 根据条件复制数据框行

Question

我有一个看起来像这样的 pandas 数据框:

hotel_id         date         length_of_stay     clicks
A               2019-01-01           3               7
B               2019-01-06           2               11
C               2019-01-03           1               4

我希望结果是:

hotel_id         date                            clicks
A               2019-01-01                          7
A               2019-01-02                          7
A               2019-01-03                          7
B               2019-01-06                          11
B               2019-01-07                          11
C               2019-01-03                          4

因此，我们可以看到有人入住该酒店每晚我们获得了多少点击...

我想不出一个优雅的方式来做到这一点..有人可以帮忙吗？

Answer 1

使用numpy.repeat() :

m= pd.DataFrame(np.repeat(df.values,df.length_of_stay,axis=0),columns=df.columns)
m['date']=m.groupby('hotel_id')['date'].transform(lambda x: pd.date_range(start=x.iloc[0], periods=len(x)))

或:

newdf = pd.DataFrame(np.repeat(df.values,df.length_of_stay,axis=0),columns=df.columns)
newdf['date'] = [i for day, n in zip(df.date,df.length_of_stay) 
                   for i in pd.date_range(start=day, periods=n)]

完整示例:

import pandas as pd
import numpy as np

data = '''\
hotel_id         date         length_of_stay     clicks
A               2019-01-01           3               7
B               2019-01-06           2               11
C               2019-01-03           1               4'''

fileobj = pd.compat.StringIO(data)
df = pd.read_csv(fileobj, parse_dates=['date'], sep='\s+')

m= pd.DataFrame(np.repeat(df.values,df.length_of_stay,axis=0),columns=df.columns)
m['date']=m.groupby('hotel_id')['date'].transform(lambda x: pd.date_range(start=x.iloc[0], periods=len(x)))
print(m)

  hotel_id       date length_of_stay clicks
0        A 2019-01-01              3      7
1        A 2019-01-02              3      7
2        A 2019-01-03              3      7
3        B 2019-01-06              2     11
4        B 2019-01-07              2     11
5        C 2019-01-03              1      4