REF: _unbox_scalar, _unbox_listlike for Categorical

pandas/core/arrays/categorical.py

@@ -93,7 +93,7 @@ def func(self, other):
 
         if is_scalar(other):
             if other in self.categories:
-                i = self.categories.get_loc(other)
+                i = self._unbox_scalar(other)
                 ret = op(self._codes, i)
 
                 if opname not in {"__eq__", "__ge__", "__gt__"}:
@@ -1184,8 +1184,7 @@ def _validate_searchsorted_value(self, value):
         # searchsorted is very performance sensitive. By converting codes
         # to same dtype as self.codes, we get much faster performance.
         if is_scalar(value):
-            codes = self.categories.get_loc(value)
-            codes = self.codes.dtype.type(codes)
+            codes = self._unbox_scalar(value)
         else:
             locs = [self.categories.get_loc(x) for x in value]
             codes = np.array(locs, dtype=self.codes.dtype)
@@ -1212,7 +1211,7 @@ def _validate_fill_value(self, fill_value):
         if isna(fill_value):
             fill_value = -1
         elif fill_value in self.categories:
-            fill_value = self.categories.get_loc(fill_value)
+            fill_value = self._unbox_scalar(fill_value)
         else:
             raise ValueError(
                 f"'fill_value={fill_value}' is not present "
@@ -1680,7 +1679,7 @@ def fillna(self, value=None, method=None, limit=None):
                     if isna(value):
                         codes[mask] = -1
                     else:
-                        codes[mask] = self.categories.get_loc(value)
+                        codes[mask] = self._unbox_scalar(value)
 
             else:
                 raise TypeError(
@@ -1734,6 +1733,17 @@ def _validate_listlike(self, target: ArrayLike) -> np.ndarray:
 
         return codes
 
+    def _unbox_scalar(self, key) -> int:
+        # searchsorted is very performance sensitive. By converting codes
+        # to same dtype as self.codes, we get much faster performance.
+        code = self.categories.get_loc(key)
+        code = self._codes.dtype.type(code)
+        return code
+
+    def _unbox_listlike(self, value):
+        unboxed = self.categories.get_indexer(value)
+        return unboxed.astype(self._ndarray.dtype, copy=False)
+
     # ------------------------------------------------------------------
 
     def take_nd(self, indexer, allow_fill: bool = False, fill_value=None):
@@ -1925,11 +1935,7 @@ def _validate_setitem_value(self, value):
                 "category, set the categories first"
             )
 
-        lindexer = self.categories.get_indexer(rvalue)
-        if isinstance(lindexer, np.ndarray) and lindexer.dtype.kind == "i":
-            lindexer = lindexer.astype(self._ndarray.dtype)
-
-        return lindexer
+        return self._unbox_listlike(rvalue)
 
     def _validate_setitem_key(self, key):
         if lib.is_integer(key):
@@ -2155,8 +2161,7 @@ def unique(self):
         return cat.set_categories(cat.categories.take(take_codes))
 
     def _values_for_factorize(self):
-        codes = self.codes.astype("int64")
-        return codes, -1
+        return self._ndarray, -1
 
     @classmethod
     def _from_factorized(cls, uniques, original):

pandas/core/arrays/datetimelike.py

@@ -697,7 +697,7 @@ def copy(self: DatetimeLikeArrayT) -> DatetimeLikeArrayT:
         return new_obj
 
     def _values_for_factorize(self):
-        return self.asi8, iNaT
+        return self._ndarray, iNaT
 
     @classmethod
     def _from_factorized(cls, values, original):

pandas/core/indexes/category.py

@@ -512,10 +512,8 @@ def _reindex_non_unique(self, target):
     # --------------------------------------------------------------------
     # Indexing Methods
 
-    def _maybe_cast_indexer(self, key):
-        code = self.categories.get_loc(key)
-        code = self.codes.dtype.type(code)
-        return code
+    def _maybe_cast_indexer(self, key) -> int:
+        return self._data._unbox_scalar(key)
 
     @Appender(_index_shared_docs["get_indexer"] % _index_doc_kwargs)
     def get_indexer(self, target, method=None, limit=None, tolerance=None):