BUG: MultiIndex slicing with negative step

doc/source/whatsnew/v1.5.0.rst

@@ -393,6 +393,9 @@ Missing
 
 MultiIndex
 ^^^^^^^^^^
+- Bug in :meth:`DataFrame.loc` returning empty result when indexer contains a slice with a negative step size and non-null start/stop values (:issue:`46156`)
+- Bug in :meth:`DataFrame.loc` raising when indexer contains a slice with a negative step size other than -1 (:issue:`46156`)
+- Bug in :meth:`DataFrame.loc` raising when indexer contains a slice with a negative step size and slicing a non-int labeled index level (:issue:`46156`)
 - Bug in :meth:`Series.to_numpy` where multiindexed Series could not be converted to numpy arrays when an ``na_value`` was supplied (:issue:`45774`)
 - Bug in :class:`MultiIndex.equals` not commutative when only one side has extension array dtype (:issue:`46026`)
 -

pandas/core/indexes/multi.py

@@ -3164,9 +3164,6 @@ def convert_indexer(start, stop, step, indexer=indexer, codes=level_codes):
             # given the inputs and the codes/indexer, compute an indexer set
             # if we have a provided indexer, then this need not consider
             # the entire labels set
-            if step is not None and step < 0:
-                # Switch elements for negative step size
-                start, stop = stop - 1, start - 1
             r = np.arange(start, stop, step)
 
             if indexer is not None and len(indexer) != len(codes):
@@ -3198,19 +3195,25 @@ def convert_indexer(start, stop, step, indexer=indexer, codes=level_codes):
         if isinstance(key, slice):
             # handle a slice, returning a slice if we can
             # otherwise a boolean indexer
+            step = key.step
+            is_negative_step = step is not None and step < 0
 
             try:
                 if key.start is not None:
                     start = level_index.get_loc(key.start)
+                elif is_negative_step:
+                    start = len(level_index) - 1
                 else:
                     start = 0
+
                 if key.stop is not None:
                     stop = level_index.get_loc(key.stop)
+                elif is_negative_step:
+                    stop = 0
                 elif isinstance(start, slice):
                     stop = len(level_index)
                 else:
                     stop = len(level_index) - 1
-                step = key.step
             except KeyError:
 
                 # we have a partial slice (like looking up a partial date
@@ -3230,8 +3233,9 @@ def convert_indexer(start, stop, step, indexer=indexer, codes=level_codes):
             elif level > 0 or self._lexsort_depth == 0 or step is not None:
                 # need to have like semantics here to right
                 # searching as when we are using a slice
-                # so include the stop+1 (so we include stop)
-                return convert_indexer(start, stop + 1, step)
+                # so adjust the stop by 1 (so we include stop)
+                stop = (stop - 1) if is_negative_step else (stop + 1)
+                return convert_indexer(start, stop, step)
             else:
                 # sorted, so can return slice object -> view
                 i = algos.searchsorted(level_codes, start, side="left")
@@ -3522,7 +3526,8 @@ def _reorder_indexer(
 
                 new_order = key_order_map[self.codes[i][indexer]]
             elif isinstance(k, slice) and k.step is not None and k.step < 0:
-                new_order = np.arange(n)[k][indexer]
+                # flip order for negative step
+                new_order = np.arange(n)[::-1][indexer]
             elif isinstance(k, slice) and k.start is None and k.stop is None:
                 # slice(None) should not determine order GH#31330
                 new_order = np.ones((n,))[indexer]

pandas/tests/indexing/multiindex/test_slice.py

@@ -758,12 +758,48 @@ def test_int_series_slicing(self, multiindex_year_month_day_dataframe_random_dat
         expected = ymd.reindex(s.index[5:])
         tm.assert_frame_equal(result, expected)
 
-    def test_loc_slice_negative_stepsize(self):
+    @pytest.mark.parametrize(
+        "dtype, loc, iloc",
+        [
+            # dtype = int, step = -1
+            ("int", slice(None, None, -1), slice(None, None, -1)),
+            ("int", slice(3, None, -1), slice(3, None, -1)),
+            ("int", slice(None, 1, -1), slice(None, 0, -1)),
+            ("int", slice(3, 1, -1), slice(3, 0, -1)),
+            # dtype = int, step = -2
+            ("int", slice(None, None, -2), slice(None, None, -2)),
+            ("int", slice(3, None, -2), slice(3, None, -2)),
+            ("int", slice(None, 1, -2), slice(None, 0, -2)),
+            ("int", slice(3, 1, -2), slice(3, 0, -2)),
+            # dtype = str, step = -1
+            ("str", slice(None, None, -1), slice(None, None, -1)),
+            ("str", slice("d", None, -1), slice(3, None, -1)),
+            ("str", slice(None, "b", -1), slice(None, 0, -1)),
+            ("str", slice("d", "b", -1), slice(3, 0, -1)),
+            # dtype = str, step = -2
+            ("str", slice(None, None, -2), slice(None, None, -2)),
+            ("str", slice("d", None, -2), slice(3, None, -2)),
+            ("str", slice(None, "b", -2), slice(None, 0, -2)),
+            ("str", slice("d", "b", -2), slice(3, 0, -2)),
+        ],
+    )
+    def test_loc_slice_negative_stepsize(self, dtype, loc, iloc):
         # GH#38071
-        mi = MultiIndex.from_product([["a", "b"], [0, 1]])
-        df = DataFrame([[1, 2], [3, 4], [5, 6], [7, 8]], index=mi)
-        result = df.loc[("a", slice(None, None, -1)), :]
-        expected = DataFrame(
-            [[3, 4], [1, 2]], index=MultiIndex.from_tuples([("a", 1), ("a", 0)])
-        )
-        tm.assert_frame_equal(result, expected)
+        labels = {
+            "str": list("abcde"),
+            "int": range(5),
+        }[dtype]
+
+        mi = MultiIndex.from_arrays([labels] * 2)
+        df = DataFrame(1.0, index=mi, columns=["A"])
+
+        SLC = pd.IndexSlice
+
+        expected = df.iloc[iloc, :]
+        result_get_loc = df.loc[SLC[loc], :]
+        result_get_locs_level_0 = df.loc[SLC[loc, :], :]
+        result_get_locs_level_1 = df.loc[SLC[:, loc], :]
+
+        tm.assert_frame_equal(result_get_loc, expected)
+        tm.assert_frame_equal(result_get_locs_level_0, expected)
+        tm.assert_frame_equal(result_get_locs_level_1, expected)