Sorry in advance if this is already discussed/reported - I searched in the archive, but didn't know exactly what to search.

Code Sample, a copy-pastable example if possible

In [2]: tt = pd.DataFrame([[1, 2, 'v1', 'v2'], [3, 4, 'v3','v4']], ...: columns=['idx1', 'idx2', 2, 6]).set_index(['idx1', 'idx2']) In [3]: tt Out[3]: 2 6 idx1 idx2 1 2 v1 v2 3 4 v3 v4 In [4]: tt.loc[1,2] Out[4]: 2 v1 6 v2 Name: (1, 2), dtype: object In [5]: tt.loc[:1,2] Out[5]: idx1 idx2 1 2 v1 Name: 2, dtype: object In [6]: tt.loc[:,2] Out[6]: idx1 idx2 1 2 v1 3 4 v3 Name: 2, dtype: object

Problem description

.loc[l1, l2] called on a MultiIndexed DataFrame is ambiguous: l2 could refer to the second level of the index, or to the columns. Apparently, the decision has been taken to follow the first interpretation, and it is fine. But then, the same must happen when l1 and l2 are slices.

I can understand that In [6] might "look different" from In [4]: but In [4] and In [5] should really give the same result (and hence In [6] too).

Expected Output

Out [4] in all three cases (or Out [6] if we prefer to favour the second interpretation - which however would probably be more disruptive).

Output of pd.show_versions()