BUG: partial-indexing on MultiIndex with IntervalIndex level

pandas/core/indexes/base.py

@@ -672,6 +672,11 @@ def _format_duplicate_message(self) -> DataFrame:
         assert len(duplicates)
 
         out = Series(np.arange(len(self))).groupby(self).agg(list)[duplicates]
+        if self._is_multi:
+            # test_format_duplicate_labels_message_multi
+            # error: "Type[Index]" has no attribute "from_tuples"  [attr-defined]
+            out.index = type(self).from_tuples(out.index)  # type: ignore[attr-defined]
+
         if self.nlevels == 1:
             out = out.rename_axis("label")
         return out.to_frame(name="positions")
@@ -5400,22 +5405,15 @@ def _get_indexer_strict(self, key, axis_name: str_t) -> tuple[Index, np.ndarray]
 
         self._raise_if_missing(keyarr, indexer, axis_name)
 
-        if (
-            needs_i8_conversion(self.dtype)
-            or is_categorical_dtype(self.dtype)
-            or is_interval_dtype(self.dtype)
-        ):
-            # For CategoricalIndex take instead of reindex to preserve dtype.
-            #  For IntervalIndex this is to map integers to the Intervals they match to.
-            keyarr = self.take(indexer)
-            if keyarr.dtype.kind in ["m", "M"]:
-                # DTI/TDI.take can infer a freq in some cases when we dont want one
-                if isinstance(key, list) or (
-                    isinstance(key, type(self))
-                    # "Index" has no attribute "freq"
-                    and key.freq is None  # type: ignore[attr-defined]
-                ):
-                    keyarr = keyarr._with_freq(None)
+        keyarr = self.take(indexer)
+        if keyarr.dtype.kind in ["m", "M"]:
+            # DTI/TDI.take can infer a freq in some cases when we dont want one
+            if isinstance(key, list) or (
+                isinstance(key, type(self))
+                # "Index" has no attribute "freq"
+                and key.freq is None  # type: ignore[attr-defined]
+            ):
+                keyarr = keyarr._with_freq(None)
 
         return keyarr, indexer
 

pandas/tests/indexing/interval/test_interval.py

@@ -133,21 +133,23 @@ def test_getitem_interval_with_nans(self, frame_or_series, indexer_sl):
 class TestIntervalIndexInsideMultiIndex:
     def test_mi_intervalindex_slicing_with_scalar(self):
         # GH#27456
+        ii = IntervalIndex.from_arrays(
+            [0, 1, 10, 11, 0, 1, 10, 11], [1, 2, 11, 12, 1, 2, 11, 12], name="MP"
+        )
         idx = pd.MultiIndex.from_arrays(
             [
                 pd.Index(["FC", "FC", "FC", "FC", "OWNER", "OWNER", "OWNER", "OWNER"]),
                 pd.Index(
                     ["RID1", "RID1", "RID2", "RID2", "RID1", "RID1", "RID2", "RID2"]
                 ),
-                IntervalIndex.from_arrays(
-                    [0, 1, 10, 11, 0, 1, 10, 11], [1, 2, 11, 12, 1, 2, 11, 12]
-                ),
+                ii,
             ]
         )
 
         idx.names = ["Item", "RID", "MP"]
         df = DataFrame({"value": [1, 2, 3, 4, 5, 6, 7, 8]})
         df.index = idx
+
         query_df = DataFrame(
             {
                 "Item": ["FC", "OWNER", "FC", "OWNER", "OWNER"],
@@ -161,5 +163,13 @@ def test_mi_intervalindex_slicing_with_scalar(self):
         idx = pd.MultiIndex.from_arrays([query_df.Item, query_df.RID, query_df.MP])
         query_df.index = idx
         result = df.value.loc[query_df.index]
-        expected = Series([1, 6, 2, 8, 7], index=idx, name="value")
+
+        # the IntervalIndex level is indexed with floats, which map to
+        #  the intervals containing them.  Matching the behavior we would get
+        #  with _only_ an IntervalIndex, we get an IntervalIndex level back.
+        sliced_level = ii.take([0, 1, 1, 3, 2])
+        expected_index = pd.MultiIndex.from_arrays(
+            [idx.get_level_values(0), idx.get_level_values(1), sliced_level]
+        )
+        expected = Series([1, 6, 2, 8, 7], index=expected_index, name="value")
         tm.assert_series_equal(result, expected)

pandas/tests/indexing/test_floats.py

@@ -512,8 +512,8 @@ def test_floating_misc(self, indexer_sl):
         for fancy_idx in [[5.0, 0.0], np.array([5.0, 0.0])]:  # float
             tm.assert_series_equal(indexer_sl(s)[fancy_idx], expected)
 
-        expected = Series([2, 0], index=Index([5, 0], dtype="int64"))
-        for fancy_idx in [[5, 0], np.array([5, 0])]:  # int
+        expected = Series([2, 0], index=Index([5, 0], dtype="float64"))
+        for fancy_idx in [[5, 0], np.array([5, 0])]:
             tm.assert_series_equal(indexer_sl(s)[fancy_idx], expected)
 
         # all should return the same as we are slicing 'the same'

pandas/tests/indexing/test_loc.py

@@ -682,7 +682,9 @@ def test_loc_setitem_frame_mixed_labels(self):
         df = DataFrame({1: [1, 2], 2: [3, 4], "a": ["a", "b"]})
 
         result = df.loc[0, [1, 2]]
-        expected = Series([1, 3], index=[1, 2], dtype=object, name=0)
+        expected = Series(
+            [1, 3], index=Index([1, 2], dtype=object), dtype=object, name=0
+        )
         tm.assert_series_equal(result, expected)
 
         expected = DataFrame({1: [5, 2], 2: [6, 4], "a": ["a", "b"]})
@@ -2414,9 +2416,6 @@ def test_loc_getitem_label_list_integer_labels(columns, column_key, expected_col
     expected = df.iloc[:, expected_columns]
     result = df.loc[["A", "B", "C"], column_key]
 
-    if df.columns.is_object() and all(isinstance(x, int) for x in column_key):
-        expected.columns = expected.columns.astype(int)
-
     tm.assert_frame_equal(result, expected, check_column_type=True)
 
 

pandas/tests/test_multilevel.py

@@ -91,14 +91,14 @@ def test_reindex_preserve_levels(
         assert chunk.index is new_index
 
         chunk = ymd.loc[new_index]
-        assert chunk.index is new_index
+        assert chunk.index.equals(new_index)
 
         ymdT = ymd.T
         chunk = ymdT.reindex(columns=new_index)
         assert chunk.columns is new_index
 
         chunk = ymdT.loc[:, new_index]
-        assert chunk.columns is new_index
+        assert chunk.columns.equals(new_index)
 
     def test_groupby_transform(self, multiindex_dataframe_random_data):
         frame = multiindex_dataframe_random_data