结合1行数据帧和正行数据帧，通过复制1行，并追加到n行数据帧

Question

考虑下面dataframes，D1作为学生信息和d2为主题级

>>> data = [['Alice',18]] 
>>> d1 = pd.DataFrame(data,columns=['Name','Age']) 
>>> d1 
    Name Age 
0 Alice 18 
>>> data2 = [['Science','A'],['Math','C'],['English','B']] 
>>> d2 = pd.DataFrame(data2, columns=['Subject','Grade']) 
>>> d2 
    Subject Grade 
0 Science  A 
1  Math  C 
2 English  B 

我想D1和D2与merge + assign + drop这样

Name Age Subject Grade 
0 Alice 18 Science  A 
1 Alice 18  Math  C 
2 Alice 18 English  B 

Answer 1

使用交叉连接结合起来 - 如果想在d1组合多个行，则此解决方案可用于：

df = pd.merge(d1.assign(A=1), d2.assign(A=1), on='A').drop('A', 1) 
print (df) 
    Name Age Subject Grade 
0 Alice 18 Science  A 
1 Alice 18  Math  C 
2 Alice 18 English  B 

与concat和reindex另一种解决方案：

df = pd.concat([d1.reindex(d2.index, method='ffill'), d2], axis=1) 
print (df) 
    Name Age Subject Grade 
0 Alice 18 Science  A 
1 Alice 18  Math  C 
2 Alice 18 English  B 

Answer 2

您可以用assign

In [101]: d2.assign(**d1.iloc[0]) 
Out[101]: 
    Subject Grade Age Name 
0 Science  A 18 Alice 
1  Math  C 18 Alice 
2 English  B 18 Alice 

或者，

In [102]: d2.assign(**d1.iloc[0].to_dict()) 
Out[102]: 
    Subject Grade Age Name 
0 Science  A 18 Alice 
1  Math  C 18 Alice 
2 English  B 18 Alice 

Answer 3

您可以使用ffill即

CONCAT做

ndf = pd.concat([d1,d2],1).ffill() 

 
    Name Age Subject Grade 
0 Alice 18.0 Science  A 
1 Alice 18.0  Math  C 
2 Alice 18.0 English  B 

Answer 4

为什么不重建Dataframe

pd.DataFrame([data[0]+x for x in data2],columns=['Name','Age','Subject', 'Grade']) 
Out[279]: 
    Name Age Subject Grade 
0 Alice 18 Science  A 
1 Alice 18  Math  C 
2 Alice 18 English  B