Closes the GH 14833 issue

Author: GSAUC3

pandas/core/indexes/multi.py

@@ -16,6 +16,7 @@
     Any,
     Literal,
     cast,
+    overload,
 )
 import warnings
 
@@ -44,6 +45,14 @@
     Shape,
     npt,
 )
+if TYPE_CHECKING:
+    from pandas._typing import (
+        NumpySorter,
+        NumpyValueArrayLike,
+        ScalarLike_co,
+    )
+
+
 from pandas.compat.numpy import function as nv
 from pandas.errors import (
     InvalidIndexError,
@@ -3778,9 +3787,25 @@ def _reorder_indexer(
         ind = np.lexsort(keys)
         return indexer[ind]
 
+    @overload
+    def searchsorted(
+        self,
+        value: ScalarLike_co,
+        side: Literal["left", "right"] = ...,
+        sirter:NumpySorter = ...,
+    ) -> np.intp:...
+
+    @overload
+    def searchsorted(
+        self,
+        value: npt.ArrayLike | ExtensionArray,
+        side: Literal["left", "right"] = ...,
+        sorter: NumpySorter = ...,
+    ) -> npt.NDArray[np.intp]:...
+
     def searchsorted(
         self,
-        value: Any,
+        value: NumpyValueArrayLike | ExtensionArray,
         side: Literal["left", "right"] = "left",
         sorter: npt.NDArray[np.intp] | None = None,
     ) -> npt.NDArray[np.intp]:
@@ -3831,6 +3856,7 @@ def searchsorted(
 
         for v, i in zip(value, indexer):
             if i != -1:
+
                 result.append(i if side == "left" else i + 1)
             else:
                 dtype = np.dtype(
@@ -3839,7 +3865,7 @@ def searchsorted(
                         for i, level in enumerate(self.levels)
                     ]
                 )
-    
+
                 val_array = np.array([v], dtype=dtype)
 
                 pos = np.searchsorted(

pandas/tests/base/test_misc.py

@@ -141,20 +141,20 @@ def test_memory_usage_components_narrow_series(any_real_numpy_dtype):
     index_usage = series.index.memory_usage()
     assert total_usage == non_index_usage + index_usage
 
-
+ 
 def test_searchsorted(request, index_or_series_obj):
     # numpy.searchsorted calls obj.searchsorted under the hood.
     # See gh-12238
     obj = index_or_series_obj
 
-    if isinstance(obj, pd.MultiIndex):
-        # See gh-14833
-        request.applymarker(
-            pytest.mark.xfail(
-                reason="np.searchsorted doesn't work on pd.MultiIndex: GH 14833"
-            )
-        )
-    elif obj.dtype.kind == "c" and isinstance(obj, Index):
+    # if isinstance(obj, pd.MultiIndex):
+    #     # See gh-14833
+    #     request.applymarker(
+    #         pytest.mark.xfail(
+    #             reason="np.searchsorted doesn't work on pd.MultiIndex: GH 14833"
+    #         )
+    #     )
+    if obj.dtype.kind == "c" and isinstance(obj, Index):
         # TODO: Should Series cases also raise? Looks like they use numpy
         #  comparison semantics https://github.com/numpy/numpy/issues/15981
         mark = pytest.mark.xfail(reason="complex objects are not comparable")

pandas/tests/indexes/multi/test_indexing.py

@@ -1032,15 +1032,14 @@ def test_get_loc_namedtuple_behaves_like_tuple():
 
 
 def test_searchsorted():
-    mi = MultiIndex.from_tuples([("a", 0), ("a", 1), ("b", 0),
-                                  ("b", 1), ("c", 0)])
+    # GH14833
+    mi = MultiIndex.from_tuples([("a", 0), ("a", 1), ("b", 0), ("b", 1), ("c", 0)])
 
     assert np.all(mi.searchsorted(("b", 0)) == 2)
     assert np.all(mi.searchsorted(("b", 0), side="right") == 3)
-
     assert np.all(mi.searchsorted(("a", 0)) == 0)
     assert np.all(mi.searchsorted(("a", -1)) == 0)
-    assert np.all(mi.searchsorted(("c", 1)) == 5)  # Beyond the last
+    assert np.all(mi.searchsorted(("c", 1)) == 5)
 
     result = mi.searchsorted([("a", 1), ("b", 0), ("c", 0)])
     expected = np.array([1, 2, 4], dtype=np.intp)
@@ -1054,4 +1053,4 @@ def test_searchsorted():
         mi.searchsorted(("a", 1), side="middle")
 
     with pytest.raises(TypeError, match="value must be a tuple or list"):
-        mi.searchsorted("a")  # not a tuple
+        mi.searchsorted("a")