DOC: Update MultiIndex.take and MultiIndex.repeat docstring examples to use MultiIndex (pandas-dev#62809)

Author: Starlessnightx

pandas/core/indexes/multi.py

@@ -2337,9 +2337,18 @@ def take(
 
         Examples
         --------
-        >>> idx = pd.Index(["a", "b", "c"])
-        >>> idx.take([2, 2, 1, 2])
-        Index(['c', 'c', 'b', 'c'], dtype='str')
+        >>> idx = pd.MultiIndex.from_arrays([["a", "b", "c"], [1, 2, 3]])
+        >>> idx
+        MultiIndex([('a', 1),
+                    ('b', 2),
+                    ('c', 3)],
+                   )
+        >>> idx.take([2, 2, 1, 0])
+        MultiIndex([('c', 3),
+                    ('c', 3),
+                    ('b', 2),
+                    ('a', 1)],
+                   )
         """
         nv.validate_take((), kwargs)
         indices = ensure_platform_int(indices)
@@ -2525,13 +2534,28 @@ def repeat(self, repeats: int, axis=None) -> MultiIndex:
 
         Examples
         --------
-        >>> idx = pd.Index(["a", "b", "c"])
+        >>> idx = pd.MultiIndex.from_arrays([["a", "b", "c"], [1, 2, 3]])
         >>> idx
-        Index(['a', 'b', 'c'], dtype='object')
+        MultiIndex([('a', 1),
+                    ('b', 2),
+                    ('c', 3)],
+                   )
         >>> idx.repeat(2)
-        Index(['a', 'a', 'b', 'b', 'c', 'c'], dtype='object')
+        MultiIndex([('a', 1),
+                    ('a', 1),
+                    ('b', 2),
+                    ('b', 2),
+                    ('c', 3),
+                    ('c', 3)],
+                   )
         >>> idx.repeat([1, 2, 3])
-        Index(['a', 'b', 'b', 'c', 'c', 'c'], dtype='object')
+        MultiIndex([('a', 1),
+                    ('b', 2),
+                    ('b', 2),
+                    ('c', 3),
+                    ('c', 3),
+                    ('c', 3)],
+                   )
         """
         nv.validate_repeat((), {"axis": axis})
         # error: Incompatible types in assignment (expression has type "ndarray",
@@ -3436,6 +3460,23 @@ def maybe_mi_droplevels(indexer, levels):
 
             return new_index
 
+        # type-check key against level(s), raise error if mismatch
+        if isinstance(key, tuple):
+            for i, k in enumerate(key):
+                if not self._is_key_type_compatible(k, i):
+                    raise TypeError(
+                        f"Type mismatch at index level {i}: "
+                        f"expected {type(self.levels[i][0]).__name__}, "
+                        f"got {type(k).__name__}"
+                    )
+        else:
+            if not self._is_key_type_compatible(key, level):
+                raise TypeError(
+                    f"Type mismatch at index level {level}: "
+                    f"expected {type(self.levels[level][0]).__name__}, "
+                    f"got {type(key).__name__}"
+                )
+
         if isinstance(level, (tuple, list)):
             if len(key) != len(level):
                 raise AssertionError(
@@ -3567,6 +3608,18 @@ def maybe_mi_droplevels(indexer, levels):
 
             return indexer, result_index
 
+    def _is_key_type_compatible(self, key, level):
+        """
+        Return True if the key type is compatible with the type of the level's values.
+        """
+        if len(self.levels[level]) == 0:
+            return True  # nothing to compare
+        level_type = type(self.levels[level][0])
+
+        # Same type → OK
+        if isinstance(key, level_type):
+            return True
+
     def _get_level_indexer(
         self, key, level: int = 0, indexer: npt.NDArray[np.bool_] | None = None
     ):

pandas/tests/indexes/multi/test_datetime_indexing.py

@@ -0,0 +1,15 @@
+import datetime
+
+import numpy as np
+import pytest
+
+import pandas as pd
+
+
+def test_multiindex_date_npdatetime_mismatch_raises():
+    idx = pd.MultiIndex.from_product(
+        [[datetime.date(2023, 11, 1)], ["A"], ["C"]], names=["date", "t1", "t2"]
+    )
+    df = pd.DataFrame({"vals": [1]}, index=idx)
+    with pytest.raises(TypeError, match="Type mismatch"):
+        df.loc[(np.datetime64("2023-11-01"), "A", "C")]