Pandas Dataframe Multiindex Merge

Question

I wanted to ask a questions regarding merging multiindex dataframe in pandas, here is a hypothetical scenario:

arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
          ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
tuples = list(zip(*arrays))
index1 = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
index2 = pd.MultiIndex.from_tuples(tuples, names=['third', 'fourth'])

s1 = pd.DataFrame(np.random.randn(8), index=index1, columns=['s1'])
s2 = pd.DataFrame(np.random.randn(8), index=index2, columns=['s2'])

Then either

s1.merge(s2, how='left', left_index=True, right_index=True)

or

s1.merge(s2, how='left', left_on=['first', 'second'], right_on=['third', 'fourth'])

will result in error.

Do I have to do reset_index() on either s1/s2 to make this work?

Answer 1

Seems like you need to use a combination of them.

s1.merge(s2, left_index=True, right_on=['third', 'fourth'])
#s1.merge(s2, right_index=True, left_on=['first', 'second'])

Output:

               s1        s2
bar one  0.765385 -0.365508
    two  1.462860  0.751862
baz one  0.304163  0.761663
    two -0.816658 -1.810634
foo one  1.891434  1.450081
    two  0.571294  1.116862
qux one  1.056516 -0.052927
    two -0.574916 -1.197596

Answer 2

Other than using the indexes names as pointed by @ALollz, you can simply use loc, which will match indexes automatically

s1.loc[:, 's2'] = s2   # Or explicitly, s2['s2']

                s1           s2
first   second      
bar     one     -0.111384   -2.341803
        two     -1.226569    1.308240
baz     one      1.880835    0.697946
        two     -0.008979   -0.247896
foo     one      0.103864   -1.039990
        two      0.836931    0.000811
qux     one     -0.859005   -1.199615
        two     -0.321341   -1.098691

A general formula would be

s1.loc[:, s2.columns] = s2