In [1]:

    

import pandas as pd
from pandas import Series, DataFrame
import numpy as np


    

In [3]:

    

df_left = DataFrame({'key': ['X', 'Y', 'Z', 'X', 'Y'], 
                    'data': range(5)})


    

In [5]:

    

df_right = DataFrame({'group_data': [10, 20]}, index=['X', 'Y'])


    

In [6]:

    

pd.merge(df_left, df_right, left_on='key', right_index=True)


    

    
Out[6]:






  

    

      

      
data
      
key
      
group_data
    
  
  

    

      
0
      
0
      
X
      
10
    
    

      
3
      
3
      
X
      
10
    
    

      
1
      
1
      
Y
      
20
    
    

      
4
      
4
      
Y
      
20
    
  

In [8]:

    

df_left_hr = DataFrame({'key1': ['SF', 'SF', 'SF', 'LA', 'LA'], 
                         'key2': [10, 20 ,30 , 20 , 30],
                           'data_set': np.arange(5)})


    

In [9]:

    

df_right_hr = DataFrame(np.arange(10).reshape(5,2),
                       index=[['LA', 'LA', 'SF', 'SF', 'SF'], 
                              [20, 10, 10, 10, 20]], columns=['col_1', 'col_2'])


    

In [10]:

    

pd.merge(df_left_hr, df_right_hr, left_on=['key1', 'key2'], right_index = True)


    

    
Out[10]:






  

    

      

      
data_set
      
key1
      
key2
      
col_1
      
col_2
    
  
  

    

      
0
      
0
      
SF
      
10
      
4
      
5
    
    

      
0
      
0
      
SF
      
10
      
6
      
7
    
    

      
1
      
1
      
SF
      
20
      
8
      
9
    
    

      
3
      
3
      
LA
      
20
      
0
      
1
    
  

In [11]:

    

df_left.join(df_right)


    

    
Out[11]:






  

    

      

      
data
      
key
      
group_data
    
  
  

    

      
0
      
0
      
X
      
NaN
    
    

      
1
      
1
      
Y
      
NaN
    
    

      
2
      
2
      
Z
      
NaN
    
    

      
3
      
3
      
X
      
NaN
    
    

      
4
      
4
      
Y
      
NaN
    
  

In [ ]: