Python Pandas - How do I join DataFrames on indices with different number of levels?

Question

Suppose I have the following 2 DataFrames:

1. df1, whose index is ['NameID', 'Date']. For example, df1 can be a panel dataset of historical salaries of employees in a company.

2. df2, whose index is ['NameID']. For example, df2 can be a dataset of employees' birthday and SSN.

What is the most efficient way to join df1 and df2 on 'NameID' as an index on a 1:m basis? DataFrame.join() doesn't allow 1:m join. I know I can first reset_index() for both df1 and df2, and then use DataFrame.merge() to join them on columns, but I think that is not efficient.

Code:

df1 = pd.DataFrame({'NameID':['A','B','C']*3,
                    'Date':['20180801']*3+['20180802']*3+['20180803']*3,
                    'Salary':np.random.rand(9)
               })
df1 = df1.set_index(['NameID', 'Date'])
df1

NameID  Date    Salary
A   20180801    0.831064
B   20180801    0.419464
C   20180801    0.239779
A   20180802    0.500048
B   20180802    0.317452
C   20180802    0.188051
A   20180803    0.076196
B   20180803    0.060435
C   20180803    0.297118

df2 = pd.DataFrame({'NameID':['A','B','C'],                   
                    'SSN':[999,888,777]
                   })
df2 = df2.set_index(['NameID'])
df2

NameID  SSN
A       999
B       888
C       777

The result I want to get is:

NameID  Date        Salary      SSN
A       20180801    0.831064    999
A       20180802    0.500048    999
A       20180803    0.076196    999
B       20180801    0.419464    888
B       20180802    0.317452    888
B       20180803    0.060435    888
C       20180801    0.239779    777
C       20180802    0.188051    777
C       20180803    0.297118    777

Answer 1

您可能要合并 。

df = pd.merge(df1, df2, on='NameID', how='left')

Answer 2

请参阅Michael B的答案，但除此之外，您可能还希望进行排序以获得所需的输出：

pd.merge(df1, df2, on='NameID', how='left').sort_values('SSN', ascending=False)

Answer 3

Answering on behalf of warwick12

df3 = pd.merge(df1, df2, left_index=True, right_index=True)