BUG: refactor _is_key_type_compatible to contemplate CI tests (GH#55969)

Author: Roxicaro

pandas/core/indexes/multi.py

@@ -31,7 +31,6 @@
  lib,
 )
 from pandas._libs.hashtable import duplicated
-from pandas._libs.tslibs.timestamps import Timestamp
 from pandas._typing import (
  AnyAll,
  AnyArrayLike,
@@ -3612,34 +3611,43 @@ def maybe_mi_droplevels(indexer, levels):
 
  def _is_key_type_compatible(self, key, level):
  """
- Return True if the key is compatible with the type of the level's values.
+ Return True if the key type is compatible with the type of the level's values.
+
+ Compatible types:
+ - int ↔ np.integer
+ - float ↔ np.floating
+ - str ↔ np.str_
+ - datetime.date ↔ datetime.datetime
+ - slices (for partial indexing)
  """
  if len(self.levels[level]) == 0:
  return True # nothing to compare
 
- level_type = self.levels[level][0]
+ level_val = self.levels[level][0]
+ level_type = type(level_val)
 
- # Allow slices (used in partial indexing)
- if isinstance(key, slice):
+ # Same type
+ if isinstance(key, level_type):
  return True
 
- # datetime/date/Timestamp compatibility
- datetime_types = (datetime.date, np.datetime64, Timestamp)
- if isinstance(level_type, datetime_types) and isinstance(
- key, datetime_types + (str,)
- ):
+ # NumPy integer / float / string compatibility
+ if isinstance(level_val, np.integer) and isinstance(key, int):
+ return True
+ if isinstance(level_val, np.floating) and isinstance(key, float):
+ return True
+ if isinstance(level_val, np.str_) and isinstance(key, str):
  return True
 
- # numeric compatibility
- if np.issubdtype(type(level_type), np.integer) and isinstance(key, int):
+ # Allow subclasses of datetime.date for datetime levels
+ if isinstance(level_val, datetime.date) and isinstance(key, datetime.date):
  return True
- if np.issubdtype(type(level_type), np.floating) and isinstance(
-  key, (int, float)
- ):
+
+ # Allow slices (used internally for partial selection)
+ if isinstance(key, slice):
  return True
 
- # string compatibility
- if isinstance(level_type, str) and isinstance(key, str):
+ # Allow any NumPy generic types for flexibility
+ if isinstance(key, np.generic):
  return True
 
  return False