Pandas select matching multi index with different number of levels

I have a data frame with 3 index levels:

       d
a b c   
1 9 4  1
2 8 2  4
3 7 5  2
4 6 4  5
5 5 6  3
6 4 5  6
7 3 7  4
8 2 6  7
9 1 8  5

and I have a multi index object with only 2 levels:

MultiIndex([(1, 9),
            (2, 8),
            (3, 7),
            (4, 6),
            (9, 1)],
           names=['a', 'b'])

How can I select the entries on the data frame that match this multi index?

Toy code:

import pandas

df1 = pandas.DataFrame(
    dict(
        a = [1,2,3,4,5,6,7,8,9],
        b = [9,8,7,6,5,4,3,2,1],
        c = [4,2,5,4,6,5,7,6,8],
        d = [1,4,2,5,3,6,4,7,5],
    )
).set_index(['a','b','c'])
select_this = multi_idx = pandas.MultiIndex.from_tuples([(1, 9), (2, 8), (3, 7), (4, 6), (9, 1)], names=['a', 'b'])

selected = df1.loc[select_this]

print(select_this)
print(df1)
print(selected)

which produces ValueError: operands could not be broadcast together with shapes (5,2) (3,) (5,2).

What I want to do can be achieved with

selected = df1.reset_index('c').loc[select_this].set_index('c', append=True)

However, this forces me to do this extra reset_index and then set_index. I want to avoid this.

>Solution :

You can filter different levels by Index.difference and filter by boolean indexing with Index.isin:

lvl = df1.index.names.difference(multi_idx.names)
out = df1[df1.index.droplevel(lvl).isin(multi_idx)]
print (out)
       d
a b c   
1 9 4  1
2 8 2  4
3 7 5  2
4 6 4  5
9 1 8  5