Backport PR #57101 on branch 2.2.x (REGR: Index.join raising TypeErro…

…r when joining an empty index to a mixed type index) (#57133)

Backport PR #57101: REGR: Index.join raising TypeError when joining an empty index to a mixed type index

Co-authored-by: Luke Manley <[email protected]>

doc/source/whatsnew/v2.2.1.rst

@@ -19,6 +19,7 @@ Fixed regressions
 - Fixed regression in :func:`wide_to_long` raising an ``AttributeError`` for string columns (:issue:`57066`)
 - Fixed regression in :meth:`DataFrameGroupBy.idxmin`, :meth:`DataFrameGroupBy.idxmax`, :meth:`SeriesGroupBy.idxmin`, :meth:`SeriesGroupBy.idxmax` ignoring the ``skipna`` argument (:issue:`57040`)
 - Fixed regression in :meth:`DataFrameGroupBy.idxmin`, :meth:`DataFrameGroupBy.idxmax`, :meth:`SeriesGroupBy.idxmin`, :meth:`SeriesGroupBy.idxmax` where values containing the minimum or maximum value for the dtype could produce incorrect results (:issue:`57040`)
+- Fixed regression in :meth:`Index.join` raising ``TypeError`` when joining an empty index to a non-empty index containing mixed dtype values (:issue:`57048`)
 - Fixed regression in :meth:`Series.pct_change` raising a ``ValueError`` for an empty :class:`Series` (:issue:`57056`)
 
 .. ---------------------------------------------------------------------------

pandas/core/indexes/base.py

@@ -4615,38 +4615,12 @@ def join(
         if level is not None and (self._is_multi or other._is_multi):
             return self._join_level(other, level, how=how)
 
-        lidx: np.ndarray | None
-        ridx: np.ndarray | None
-
-        if len(other) == 0:
-            if how in ("left", "outer"):
-                if sort and not self.is_monotonic_increasing:
-                    lidx = self.argsort()
-                    join_index = self.take(lidx)
-                else:
-                    lidx = None
-                    join_index = self._view()
-                ridx = np.broadcast_to(np.intp(-1), len(join_index))
-                return join_index, lidx, ridx
-            elif how in ("right", "inner", "cross"):
-                join_index = other._view()
-                lidx = np.array([], dtype=np.intp)
-                return join_index, lidx, None
-
-        if len(self) == 0:
-            if how in ("right", "outer"):
-                if sort and not other.is_monotonic_increasing:
-                    ridx = other.argsort()
-                    join_index = other.take(ridx)
-                else:
-                    ridx = None
-                    join_index = other._view()
-                lidx = np.broadcast_to(np.intp(-1), len(join_index))
-                return join_index, lidx, ridx
-            elif how in ("left", "inner", "cross"):
-                join_index = self._view()
-                ridx = np.array([], dtype=np.intp)
-                return join_index, None, ridx
+        if len(self) == 0 or len(other) == 0:
+            try:
+                return self._join_empty(other, how, sort)
+            except TypeError:
+                # object dtype; non-comparable objects
+                pass
 
         if self.dtype != other.dtype:
             dtype = self._find_common_type_compat(other)
@@ -4681,6 +4655,33 @@ def join(
 
         return self._join_via_get_indexer(other, how, sort)
 
+    @final
+    def _join_empty(
+        self, other: Index, how: JoinHow, sort: bool
+    ) -> tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
+        assert len(self) == 0 or len(other) == 0
+        _validate_join_method(how)
+
+        lidx: np.ndarray | None
+        ridx: np.ndarray | None
+
+        if len(other):
+            how = cast(JoinHow, {"left": "right", "right": "left"}.get(how, how))
+            join_index, ridx, lidx = other._join_empty(self, how, sort)
+        elif how in ["left", "outer"]:
+            if sort and not self.is_monotonic_increasing:
+                lidx = self.argsort()
+                join_index = self.take(lidx)
+            else:
+                lidx = None
+                join_index = self._view()
+            ridx = np.broadcast_to(np.intp(-1), len(join_index))
+        else:
+            join_index = other._view()
+            lidx = np.array([], dtype=np.intp)
+            ridx = None
+        return join_index, lidx, ridx
+
     @final
     def _join_via_get_indexer(
         self, other: Index, how: JoinHow, sort: bool

pandas/tests/reshape/merge/test_join.py

@@ -1042,6 +1042,25 @@ def test_join_empty(left_empty, how, exp):
     tm.assert_frame_equal(result, expected)
 
 
+def test_join_empty_uncomparable_columns():
+    # GH 57048
+    df1 = DataFrame()
+    df2 = DataFrame(columns=["test"])
+    df3 = DataFrame(columns=["foo", ("bar", "baz")])
+
+    result = df1 + df2
+    expected = DataFrame(columns=["test"])
+    tm.assert_frame_equal(result, expected)
+
+    result = df2 + df3
+    expected = DataFrame(columns=[("bar", "baz"), "foo", "test"])
+    tm.assert_frame_equal(result, expected)
+
+    result = df1 + df3
+    expected = DataFrame(columns=[("bar", "baz"), "foo"])
+    tm.assert_frame_equal(result, expected)
+
+
 @pytest.mark.parametrize(
     "how, values",
     [