BUG: Fix MultiIndex .loc with all numpy arrays

Author: spacesphere

doc/source/whatsnew/v0.23.0.txt

@@ -767,6 +767,7 @@ Indexing
 - Bug in :class:`IntervalIndex` where empty and purely NA data was constructed inconsistently depending on the construction method (:issue:`18421`)
 - Bug in :func:`IntervalIndex.symmetric_difference` where the symmetric difference with a non-``IntervalIndex`` did not raise (:issue:`18475`)
 - Bug in :class:`IntervalIndex` where set operations that returned an empty ``IntervalIndex`` had the wrong dtype (:issue:`19101`)
+- Bug in indexing where nested indexers having only numpy arrays are handled incorrectly (:issue:`19686`)
 
 MultiIndex
 ^^^^^^^^^^

pandas/core/indexing.py

@@ -2107,10 +2107,9 @@ def is_nested_tuple(tup, labels):
     if not isinstance(tup, tuple):
         return False
 
-    # are we nested tuple of: tuple,list,slice
     for i, k in enumerate(tup):
 
-        if isinstance(k, (tuple, list, slice)):
+        if is_list_like(k) or isinstance(k, slice):
             return isinstance(labels, MultiIndex)
 
     return False

pandas/tests/indexing/test_loc.py

@@ -711,3 +711,36 @@ def test_identity_slice_returns_new_object(self):
 
         original_series[:3] = [7, 8, 9]
         assert all(sliced_series[:3] == [7, 8, 9])
+
+    @pytest.mark.parametrize('indexer_type_1',
+                             (list, tuple, set, slice, np.ndarray, Series))
+    @pytest.mark.parametrize('indexer_type_2',
+                             (list, tuple, set, slice, np.ndarray, Series))
+    def test_loc_getitem_nested_indexer(self, indexer_type_1, indexer_type_2):
+
+        def convert_nested_indexer(indexer_type, keys):
+            if indexer_type == np.ndarray:
+                return np.array(keys)
+            if indexer_type == slice:
+                return slice(*keys)
+            return indexer_type(keys)
+
+        a = [10, 20, 30]
+        b = [1, 2, 3]
+        index = pd.MultiIndex.from_product([a, b])
+        df = pd.DataFrame(np.arange(len(index)), index=index, columns=['Data'])
+
+        keys = ([10, 20], [2, 3])
+        types = (indexer_type_1, indexer_type_2)
+        # check indexers with all the combinations of nested objects
+        # of all the valid types
+        indexer = tuple(
+            convert_nested_indexer(indexer_type, k)
+            for indexer_type, k in zip(types, keys))
+
+        result = df.loc[indexer, 'Data']
+        expected = pd.Series(
+            [1, 2, 4, 5], name='Data',
+            index=pd.MultiIndex.from_product(keys))
+
+        tm.assert_series_equal(result, expected)