REF: implement Categorical._validate_setitem_value

pandas/core/arrays/categorical.py

@@ -9,7 +9,7 @@
 
 from pandas._config import get_option
 
-from pandas._libs import NaT, algos as libalgos, hashtable as htable
+from pandas._libs import NaT, algos as libalgos, hashtable as htable, lib
 from pandas._typing import ArrayLike, Dtype, Ordered, Scalar
 from pandas.compat.numpy import function as nv
 from pandas.util._decorators import cache_readonly, deprecate_kwarg, doc
@@ -1868,14 +1868,6 @@ def __repr__(self) -> str:
 
     # ------------------------------------------------------------------
 
-    def _maybe_coerce_indexer(self, indexer):
-        """
-        return an indexer coerced to the codes dtype
-        """
-        if isinstance(indexer, np.ndarray) and indexer.dtype.kind == "i":
-            indexer = indexer.astype(self._codes.dtype)
-        return indexer
-
     def __getitem__(self, key):
         """
         Return an item.
@@ -1905,6 +1897,11 @@ def __setitem__(self, key, value):
             If (one or more) Value is not in categories or if a assigned
             `Categorical` does not have the same categories
         """
+        key = self._validate_setitem_key(key)
+        value = self._validate_setitem_value(value)
+        self._ndarray[key] = value
+
+    def _validate_setitem_value(self, value):
         value = extract_array(value, extract_numpy=True)
 
         # require identical categories set
@@ -1934,12 +1931,19 @@ def __setitem__(self, key, value):
                 "category, set the categories first"
             )
 
-        # set by position
-        if isinstance(key, (int, np.integer)):
+        lindexer = self.categories.get_indexer(rvalue)
+        if isinstance(lindexer, np.ndarray) and lindexer.dtype.kind == "i":
+            lindexer = lindexer.astype(self._ndarray.dtype)
+
+        return lindexer
+
+    def _validate_setitem_key(self, key):
+        if lib.is_integer(key):
+            # set by position
             pass
 
-        # tuple of indexers (dataframe)
         elif isinstance(key, tuple):
+            # tuple of indexers (dataframe)
             # only allow 1 dimensional slicing, but can
             # in a 2-d case be passed (slice(None),....)
             if len(key) == 2:
@@ -1951,17 +1955,14 @@ def __setitem__(self, key, value):
             else:
                 raise AssertionError("invalid slicing for a 1-ndim categorical")
 
-        # slicing in Series or Categorical
         elif isinstance(key, slice):
+            # slicing in Series or Categorical
             pass
 
         # else: array of True/False in Series or Categorical
 
-        lindexer = self.categories.get_indexer(rvalue)
-        lindexer = self._maybe_coerce_indexer(lindexer)
-
         key = check_array_indexer(self, key)
-        self._codes[key] = lindexer
+        return key
 
     def _reverse_indexer(self) -> Dict[Hashable, np.ndarray]:
         """

pandas/core/arrays/datetimelike.py

@@ -546,6 +546,15 @@ def __getitem__(self, key):
                 return self._box_func(result)
             return self._simple_new(result, dtype=self.dtype)
 
+        key = self._validate_getitem_key(key)
+        result = self._ndarray[key]
+        if lib.is_scalar(result):
+            return self._box_func(result)
+
+        freq = self._get_getitem_freq(key)
+        return self._simple_new(result, dtype=self.dtype, freq=freq)
+
+    def _validate_getitem_key(self, key):
         if com.is_bool_indexer(key):
             # first convert to boolean, because check_array_indexer doesn't
             # allow object dtype
@@ -560,12 +569,7 @@ def __getitem__(self, key):
             pass
         else:
             key = check_array_indexer(self, key)
-
-        freq = self._get_getitem_freq(key)
-        result = self._ndarray[key]
-        if lib.is_scalar(result):
-            return self._box_func(result)
-        return self._simple_new(result, dtype=self.dtype, freq=freq)
+        return key
 
     def _get_getitem_freq(self, key):
         """