Consider The following Example Code:

import pandas as pd

df1 = pd.DataFrame({'data1':[1,2,3,4]}, index=pd.MultiIndex.from_product([['a','b'],[1,2],], names=['index1','Index2']))


df2 = pd.DataFrame({'data2':[5,6]}, index=pd.MultiIndex.from_product([[1,2]], names=['Index2']))


df3 = df1.join(df2, how='left',sort=False)

If I print df1, The index is in Order as I expect it:

               data1
index1 Index2       
a      1           1
       2           2
b      1           3
       2           4

Now I want to add additional data with df2:

        data2
Index2       
1           5
2           6

but after the merge with df2, the Order of the Index has changed. Index2 is now the first level. I explicitly tried to forbid it with using sort=False, but it still promotes the joined index to the first level:

print(df3)

               data1  data2
Index2 index1              
1      a           1      5
       b           3      5
2      a           2      6
       b           4      6

The pandas documentation states, that how='left' does accomplish that, but it does not seem to work.

Is there a way i can enforce the resulting index_columns of the merge to be in the same order as df1? Like this:

               data1  data2
Index1 index2              
a     1           1      5
b      1           3      5
a      2           2      6
b      2           4      6

>Solution :

You can swaplevel and reindex:

df3 = df1.join(df2, how='left').swaplevel().reindex(df1.index)

Or reorder_levels:

df3 = df1.join(df2, how='left').reorder_levels(df1.index.names).reindex(df1.index)

Output:

               data1  data2
index1 Index2              
a      1           1      5
       2           2      6
b      1           3      5
       2           4      6