How to join pandas dataframe on 2 columns?

Question

Assume the following DataFrames

df1:

id    data1
1     10
2     200
3     3000
4     40000

df2:

id1    id2    data2
1      2      210
1      3      3010
1      4      40010
2      3      3200
2      4      40200
3      4      43000

I want the new df3:

id1    id2    data2    data11    data12        
1      2      210      10        200
1      3      3010     10        3000
1      4      40010    10        40000 
2      3      3200     200       3000
2      4      40200    200       40000
3      4      43000    3000      40000

What is the correct way to achieve this in pandas?

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Edit: Please not the specific data can be arbitrary. I chose this specific data just to show where everything comes from, but every data element has no correlation to any other data element.

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Other dataframes examples, because the first one wasn't clear enough:

df4:

id    data1
1     a
2     b
3     c
4     d

df5:

id1    id2    data2
1      2      e
1      3      f
1      4      g
2      3      h
2      4      i
3      4      j

I want the new df6:

id1    id2    data2    data11    data12        
1      2      e        a         b  
1      3      f        a         c
1      4      g        a         d
2      3      h        b         c
2      4      i        b         d
3      4      j        c         d

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Edit2: Data11 and Data12 are simply a copy of data1, with the corresponding id id1 or id2

Answer 1

1.First merge both dataframe using id1 and id column
2.rename data1 as data11
3. drop id column
4. Now merge df1 and df3 on id2 and id

df3 = pd.merge(df2,df1,left_on=['id1'],right_on=['id'],how='left')
df3.rename(columns={'data1':'data11'},inplace=True)
df3.drop('id',axis=1,inplace=True)

df3 = pd.merge(d3,df1,left_on=['id2'],right_on=['id'],how='left')
df3.rename(columns={'data1':'data12'},inplace=True)
df3.drop('id',axis=1,inplace=True)

I hope it would solve your problem