BUG: raise KeyError if MultiIndex.get_loc_level is asked unused label

closes pandas-dev#22221

Author: toobaz

doc/source/whatsnew/v0.24.0.txt

@@ -622,8 +622,8 @@ Missing
 MultiIndex
 ^^^^^^^^^^
 
-- Removed compatibility for MultiIndex pickles prior to version 0.8.0; compatibility with MultiIndex pickles from version 0.13 forward is maintained (:issue:`21654`)
--
+- Removed compatibility for :class:`MultiIndex` pickles prior to version 0.8.0; compatibility with :class:`MultiIndex` pickles from version 0.13 forward is maintained (:issue:`21654`)
+- :meth:`MultiIndex.get_loc_level` (and as a consequence, ``.loc`` on a :class:``MultiIndex``ed object) will now raise a ``KeyError``, rather than returning an empty ``slice``, if asked a label which is present in the ``levels`` but is unused (:issue:`22221`)
 -
 
 I/O

pandas/core/indexes/multi.py

@@ -2185,11 +2185,6 @@ def _maybe_to_slice(loc):
 
         if not isinstance(key, tuple):
             loc = self._get_level_indexer(key, level=0)
-
-            # _get_level_indexer returns an empty slice if the key has
-            # been dropped from the MultiIndex
-            if isinstance(loc, slice) and loc.start == loc.stop:
-                raise KeyError(key)
             return _maybe_to_slice(loc)
 
         keylen = len(key)
@@ -2451,6 +2446,9 @@ def convert_indexer(start, stop, step, indexer=indexer, labels=labels):
 
             i = labels.searchsorted(loc, side='left')
             j = labels.searchsorted(loc, side='right')
+            if i == j:
+                # The label is present in self.levels[level] but unused:
+                raise KeyError(key)
             return slice(i, j)
 
     def get_locs(self, seq):

pandas/tests/indexes/multi/test_indexing.py

@@ -271,6 +271,8 @@ def test_get_loc_level():
     assert new_index is None
 
     pytest.raises(KeyError, index.get_loc_level, (2, 2))
+    # GH 22221: unused label
+    pytest.raises(KeyError, index.drop(2).get_loc_level, 2)
 
     index = MultiIndex(levels=[[2000], lrange(4)], labels=[np.array(
         [0, 0, 0, 0]), np.array([0, 1, 2, 3])])