Backport PR #31631 on branch 1.0.x (REGR: Fixed setitem with MultiIndex)

doc/source/whatsnew/v1.0.1.rst

@@ -17,6 +17,7 @@ Fixed regressions
 
 - Fixed regression in :class:`DataFrame` setting values with a slice (e.g. ``df[-4:] = 1``) indexing by label instead of position (:issue:`31469`)
 - Fixed regression when indexing a ``Series`` or ``DataFrame`` indexed by ``DatetimeIndex`` with a slice containg a :class:`datetime.date` (:issue:`31501`)
+- Fixed regression in ``DataFrame.__setitem__`` raising an ``AttributeError`` with a :class:`MultiIndex` and a non-monotonic indexer (:issue:`31449`)
 - Fixed regression in :class:`Series` multiplication when multiplying a numeric :class:`Series` with >10000 elements with a timedelta-like scalar (:issue:`31457`)
 - Fixed regression in :meth:`GroupBy.apply` if called with a function which returned a non-pandas non-scalar object (e.g. a list or numpy array) (:issue:`31441`)
 - Fixed regression in :meth:`to_datetime` when parsing non-nanosecond resolution datetimes (:issue:`31491`)

pandas/core/indexing.py

@@ -926,7 +926,8 @@ def _setitem_with_indexer(self, indexer, value):
 
                     # we can directly set the series here
                     # as we select a slice indexer on the mi
-                    idx = index._convert_slice_indexer(idx)
+                    if isinstance(idx, slice):
+                        idx = index._convert_slice_indexer(idx)
                     obj._consolidate_inplace()
                     obj = obj.copy()
                     obj._data = obj._data.setitem(indexer=tuple([idx]), value=value)

pandas/tests/indexing/multiindex/test_setitem.py

@@ -414,6 +414,16 @@ def test_astype_assignment_with_dups(self):
         df["A"] = df["A"].astype(np.float64)
         tm.assert_index_equal(df.index, index)
 
+    def test_setitem_nonmonotonic(self):
+        # https://github.com/pandas-dev/pandas/issues/31449
+        index = pd.MultiIndex.from_tuples(
+            [("a", "c"), ("b", "x"), ("a", "d")], names=["l1", "l2"]
+        )
+        df = pd.DataFrame(data=[0, 1, 2], index=index, columns=["e"])
+        df.loc["a", "e"] = np.arange(99, 101, dtype="int64")
+        expected = pd.DataFrame({"e": [99, 1, 100]}, index=index)
+        tm.assert_frame_equal(df, expected)
+
 
 def test_frame_setitem_view_direct(multiindex_dataframe_random_data):
     # this works because we are modifying the underlying array