如何从另一个数据帧映射Pandas数据帧w.r.t索引和列中的数据

假设我有两个数据帧，如下所示：

DF1:

from datetime import date, timedelta
import pandas as pd
import numpy as np
sdate = date(2019,1,1)   # start date
edate = date(2019,1,7)   # end date

required_dates = pd.date_range(sdate,edate-timedelta(days=1),freq='d')
# initialize list of lists 
data = [['2019-01-01', 1001], ['2019-01-03', 1121] ,['2019-01-02', 1500], 
        ['2019-01-02', 1400],['2019-01-04', 1501],['2019-01-01', 1200],
        ['2019-01-04', 1201],['2019-01-04', 1551],['2019-01-05', 1400]]
# Create the pandas DataFrame 
df1 = pd.DataFrame(data, columns = ['OnlyDate', 'TBID']) 
df1.sort_values(by='OnlyDate',inplace=True)
df1     
     OnlyDate   TBID
0   2019-01-01  1001
5   2019-01-01  1200
2   2019-01-02  1500
3   2019-01-02  1400
1   2019-01-03  1121
4   2019-01-04  1501
6   2019-01-04  1201
7   2019-01-04  1551
8   2019-01-05  1400

df2=pd.DataFrame(columns=[sorted(df1['TBID'].unique())],index=required_dates)
df2     
            1001    1121    1200    1201    1400    1500    1501    1551
2019-01-01  NaN     NaN     NaN     NaN     NaN     NaN     NaN      NaN
2019-01-02  NaN     NaN     NaN     NaN     NaN     NaN     NaN      NaN
2019-01-03  NaN     NaN     NaN     NaN     NaN     NaN     NaN      NaN
2019-01-04  NaN     NaN     NaN     NaN     NaN     NaN     NaN      NaN
2019-01-05  NaN     NaN     NaN     NaN     NaN     NaN     NaN      NaN
2019-01-06  NaN     NaN     NaN     NaN     NaN     NaN     NaN      NaN

DF2:

from datetime import date, timedelta
import pandas as pd
import numpy as np
sdate = date(2019,1,1)   # start date
edate = date(2019,1,7)   # end date

required_dates = pd.date_range(sdate,edate-timedelta(days=1),freq='d')
# initialize list of lists 
data = [['2019-01-01', 1001], ['2019-01-03', 1121] ,['2019-01-02', 1500], 
        ['2019-01-02', 1400],['2019-01-04', 1501],['2019-01-01', 1200],
        ['2019-01-04', 1201],['2019-01-04', 1551],['2019-01-05', 1400]]
# Create the pandas DataFrame 
df1 = pd.DataFrame(data, columns = ['OnlyDate', 'TBID']) 
df1.sort_values(by='OnlyDate',inplace=True)
df1     
     OnlyDate   TBID
0   2019-01-01  1001
5   2019-01-01  1200
2   2019-01-02  1500
3   2019-01-02  1400
1   2019-01-03  1121
4   2019-01-04  1501
6   2019-01-04  1201
7   2019-01-04  1551
8   2019-01-05  1400

df2=pd.DataFrame(columns=[sorted(df1['TBID'].unique())],index=required_dates)
df2     
            1001    1121    1200    1201    1400    1500    1501    1551
2019-01-01  NaN     NaN     NaN     NaN     NaN     NaN     NaN      NaN
2019-01-02  NaN     NaN     NaN     NaN     NaN     NaN     NaN      NaN
2019-01-03  NaN     NaN     NaN     NaN     NaN     NaN     NaN      NaN
2019-01-04  NaN     NaN     NaN     NaN     NaN     NaN     NaN      NaN
2019-01-05  NaN     NaN     NaN     NaN     NaN     NaN     NaN      NaN
2019-01-06  NaN     NaN     NaN     NaN     NaN     NaN     NaN      NaN

我试图将（True或1）应用于此DF3数据帧w.r.t，并应用于df1的值，如以下输出：

df3 =df2.copy()
for index, row in df1.iterrows():
    df3.loc[row['OnlyDate'],row['TBID']] = 1

df3.fillna(0, inplace=True)
df3 


            1001    1121    1200    1201    1400    1500    1501    1551
2019-01-01   1       0       1       0       0       0       0       0
2019-01-02   0       0       0       0       1       1       0       0
2019-01-03   0       1       0       0       0       0       0       0
2019-01-04   0       0       0       1       0       0       1       1
2019-01-05   0       0       0       0       1       0       0       0
2019-01-06   0       0       0       0       0       0       0       0

有更好的方法吗？

与

max

一起用于指示器（始终

0，1

）或

求和

如果需要计数值：

df = pd.get_dummies(df1.set_index('OnlyDate')['TBID']).max(level=0)
print (df)
            1001  1121  1200  1201  1400  1500  1501  1551
OnlyDate                                                  
2019-01-01     1     0     1     0     0     0     0     0
2019-01-02     0     0     0     0     1     1     0     0
2019-01-03     0     1     0     0     0     0     0     0
2019-01-04     0     0     0     1     0     0     1     1
2019-01-05     0     0     0     0     1     0     0     0