BUG: .get_indexer_non_unique() must return an array of ints (pandas-dev#44084) (pandas-dev#44404)

Author: johannes-mueller

doc/source/whatsnew/v1.4.0.rst

@@ -573,7 +573,7 @@ Strings
 
 Interval
 ^^^^^^^^
--
+- Bug in :meth:`IntervalIndex.get_indexer_non_unique` returning boolean mask instead of array of integers for a non unique and non monotonic index (:issue:`44084`)
 -
 
 Indexing

pandas/core/indexes/interval.py

@@ -727,6 +727,8 @@ def _get_indexer_pointwise(
                 if isinstance(locs, slice):
                     # Only needed for get_indexer_non_unique
                     locs = np.arange(locs.start, locs.stop, locs.step, dtype="intp")
+                elif not self.is_unique and not self.is_monotonic:
+                    locs = np.where(locs)[0]
                 locs = np.array(locs, ndmin=1)
             except KeyError:
                 missing.append(i)

pandas/tests/indexes/interval/test_indexing.py

@@ -8,8 +8,10 @@
 from pandas import (
     NA,
     CategoricalIndex,
+    Index,
     Interval,
     IntervalIndex,
+    MultiIndex,
     NaT,
     Series,
     Timedelta,
@@ -374,6 +376,31 @@ def test_get_indexer_with_nans(self):
         expected = np.array([0, 1], dtype=np.intp)
         tm.assert_numpy_array_equal(result, expected)
 
+    def test_get_index_non_unique_non_monotonic(self):
+        # GH#44084 (root cause)
+        index = IntervalIndex.from_tuples(
+            [(0.0, 1.0), (1.0, 2.0), (0.0, 1.0), (1.0, 2.0)]
+        )
+
+        result, _ = index.get_indexer_non_unique([Interval(1.0, 2.0)])
+        expected = np.array([1, 3], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_get_indexer_multiindex_with_intervals(self):
+        # GH#44084 (MultiIndex case as reported)
+        interval_index = IntervalIndex.from_tuples(
+            [(2.0, 3.0), (0.0, 1.0), (1.0, 2.0)], name="interval"
+        )
+        foo_index = Index([1, 2, 3], name="foo")
+
+        multi_index = MultiIndex.from_product([foo_index, interval_index])
+
+        result = multi_index.get_level_values("interval").get_indexer_for(
+            [Interval(0.0, 1.0)]
+        )
+        expected = np.array([1, 4, 7], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
 
 class TestSliceLocs:
     def test_slice_locs_with_interval(self):