ENH: EA._from_scalars

pandas/core/arrays/categorical.py

@@ -494,6 +494,22 @@ def _from_sequence(
     ) -> Self:
         return cls(scalars, dtype=dtype, copy=copy)
 
+    @classmethod
+    def _from_scalars(cls, scalars, dtype=None):
+        if dtype is None:
+            # The _from_scalars strictness doesn't make much sense in this case.
+            raise NotImplementedError
+
+        res = cls._from_sequence(scalars, dtype=dtype)
+
+        # if there are any non-category elements in scalars, these will be
+        #  converted to NAs in res.
+        mask = isna(scalars)
+        if not (mask == res.isna()).all():
+            # Some non-category element in scalars got converted to NA in res.
+            raise ValueError
+        return res
+
     @overload
     def astype(self, dtype: npt.DTypeLike, copy: bool = ...) -> np.ndarray:
         ...

pandas/core/arrays/datetimes.py

@@ -258,6 +258,14 @@ def _scalar_type(self) -> type[Timestamp]:
     _freq: BaseOffset | None = None
     _default_dtype = DT64NS_DTYPE  # used in TimeLikeOps.__init__
 
+    @classmethod
+    def _from_scalars(cls, scalars, dtype=None):
+        if lib.infer_dtype(scalars, skipna=True) not in ["datetime", "datetime64"]:
+            # TODO: require any NAs be valid-for-DTA
+            # TODO: if dtype is passed, check for tzawareness compat?
+            raise ValueError
+        return cls._from_sequence(scalars, dtype=dtype)
+
     @classmethod
     def _validate_dtype(cls, values, dtype):
         # used in TimeLikeOps.__init__

pandas/core/arrays/string_.py

@@ -57,6 +57,7 @@
         NumpySorter,
         NumpyValueArrayLike,
         Scalar,
+        Self,
         npt,
         type_t,
     )
@@ -228,6 +229,13 @@ def tolist(self):
             return [x.tolist() for x in self]
         return list(self.to_numpy())
 
+    @classmethod
+    def _from_scalars(cls, scalars, dtype=None) -> Self:
+        if lib.infer_dtype(scalars, skipna=True) != "string":
+            # TODO: require any NAs be valid-for-string
+            raise ValueError
+        return cls._from_sequence(scalars, dtype=dtype)
+
 
 # error: Definition of "_concat_same_type" in base class "NDArrayBacked" is
 # incompatible with definition in base class "ExtensionArray"

pandas/core/dtypes/cast.py

@@ -455,16 +455,11 @@ def maybe_cast_pointwise_result(
     """
 
     if isinstance(dtype, ExtensionDtype):
-        if not isinstance(dtype, (CategoricalDtype, DatetimeTZDtype)):
-            # TODO: avoid this special-casing
-            # We have to special case categorical so as not to upcast
-            # things like counts back to categorical
-
-            cls = dtype.construct_array_type()
-            if same_dtype:
-                result = _maybe_cast_to_extension_array(cls, result, dtype=dtype)
-            else:
-                result = _maybe_cast_to_extension_array(cls, result)
+        cls = dtype.construct_array_type()
+        if same_dtype:
+            result = _maybe_cast_to_extension_array(cls, result, dtype=dtype)
+        else:
+            result = _maybe_cast_to_extension_array(cls, result)
 
     elif (numeric_only and dtype.kind in "iufcb") or not numeric_only:
         result = maybe_downcast_to_dtype(result, dtype)
@@ -489,11 +484,17 @@ def _maybe_cast_to_extension_array(
     -------
     ExtensionArray or obj
     """
-    from pandas.core.arrays.string_ import BaseStringArray
 
-    # Everything can be converted to StringArrays, but we may not want to convert
-    if issubclass(cls, BaseStringArray) and lib.infer_dtype(obj) != "string":
-        return obj
+    if hasattr(cls, "_from_scalars"):
+        # TODO: get this everywhere!
+        try:
+            result = cls._from_scalars(obj, dtype=dtype)
+        except (TypeError, ValueError, NotImplementedError):
+            # TODO: document that _from_scalars should only raise ValueError
+            #  or TypeError; NotImplementedError is here until we decide what
+            #  to do for Categorical.
+            return obj
+        return result
 
     try:
         result = cls._from_sequence(obj, dtype=dtype)

pandas/tests/resample/test_timedelta.py

@@ -103,7 +103,7 @@ def test_resample_categorical_data_with_timedeltaindex():
         index=pd.TimedeltaIndex([0, 10], unit="s", freq="10s"),
     )
     expected = expected.reindex(["Group_obj", "Group"], axis=1)
-    expected["Group"] = expected["Group_obj"]
+    expected["Group"] = expected["Group_obj"].astype("category")
     tm.assert_frame_equal(result, expected)