ENH: EA._from_scalars

pandas/core/arrays/base.py

@@ -18,6 +18,7 @@
     cast,
     overload,
 )
+import warnings
 
 import numpy as np
 
@@ -33,6 +34,7 @@
     Substitution,
     cache_readonly,
 )
+from pandas.util._exceptions import find_stack_level
 from pandas.util._validators import (
     validate_bool_kwarg,
     validate_fillna_kwargs,
@@ -83,6 +85,7 @@
         AstypeArg,
         AxisInt,
         Dtype,
+        DtypeObj,
         FillnaOptions,
         InterpolateOptions,
         NumpySorter,
@@ -280,6 +283,38 @@ def _from_sequence(cls, scalars, *, dtype: Dtype | None = None, copy: bool = Fal
         """
         raise AbstractMethodError(cls)
 
+    @classmethod
+    def _from_scalars(cls, scalars, *, dtype: DtypeObj) -> Self:
+        """
+        Strict analogue to _from_sequence, allowing only sequences of scalars
+        that should be specifically inferred to the given dtype.
+
+        Parameters
+        ----------
+        scalars : sequence
+        dtype : ExtensionDtype
+
+        Raises
+        ------
+        TypeError or ValueError
+
+        Notes
+        -----
+        This is called in a try/except block when casting the result of a
+        pointwise operation.
+        """
+        try:
+            return cls._from_sequence(scalars, dtype=dtype, copy=False)
+        except (ValueError, TypeError):
+            raise
+        except Exception:
+            warnings.warn(
+                "_from_scalars should only raise ValueError or TypeError. "
+                "Consider overriding _from_scalars where appropriate.",
+                stacklevel=find_stack_level(),
+            )
+            raise
+
     @classmethod
     def _from_sequence_of_strings(
         cls, strings, *, dtype: Dtype | None = None, copy: bool = False

pandas/core/arrays/categorical.py

@@ -101,6 +101,7 @@
         AstypeArg,
         AxisInt,
         Dtype,
+        DtypeObj,
         NpDtype,
         Ordered,
         Self,
@@ -509,6 +510,22 @@ def _from_sequence(
     ) -> Self:
         return cls(scalars, dtype=dtype, copy=copy)
 
+    @classmethod
+    def _from_scalars(cls, scalars, *, dtype: DtypeObj) -> Self:
+        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

@@ -74,6 +74,7 @@
 
     from pandas._typing import (
         DateTimeErrorChoices,
+        DtypeObj,
         IntervalClosedType,
         Self,
         TimeAmbiguous,
@@ -263,6 +264,14 @@ 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: DtypeObj) -> Self:
+        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

@@ -54,9 +54,11 @@
     from pandas._typing import (
         AxisInt,
         Dtype,
+        DtypeObj,
         NumpySorter,
         NumpyValueArrayLike,
         Scalar,
+        Self,
         npt,
         type_t,
     )
@@ -228,6 +230,13 @@ def tolist(self):
             return [x.tolist() for x in self]
         return list(self.to_numpy())
 
+    @classmethod
+    def _from_scalars(cls, scalars, dtype: DtypeObj) -> 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

@@ -461,16 +461,11 @@ 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)
@@ -495,11 +490,14 @@ def _maybe_cast_to_extension_array(
     -------
     ExtensionArray or obj
     """
-    from pandas.core.arrays.string_ import BaseStringArray
+    result: ArrayLike
 
-    # 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 dtype is not None:
+        try:
+            result = cls._from_scalars(obj, dtype=dtype)
+        except (TypeError, ValueError):
+            return obj
+        return result
 
     try:
         result = cls._from_sequence(obj, dtype=dtype)

pandas/core/series.py

@@ -77,6 +77,7 @@
 )
 from pandas.core.dtypes.dtypes import (
     ArrowDtype,
+    CategoricalDtype,
     ExtensionDtype,
 )
 from pandas.core.dtypes.generic import ABCDataFrame
@@ -102,6 +103,7 @@
 from pandas.core.arrays import ExtensionArray
 from pandas.core.arrays.categorical import CategoricalAccessor
 from pandas.core.arrays.sparse import SparseAccessor
+from pandas.core.arrays.string_ import StringDtype
 from pandas.core.construction import (
     extract_array,
     sanitize_array,
@@ -3331,7 +3333,12 @@ def combine(
 
         # try_float=False is to match agg_series
         npvalues = lib.maybe_convert_objects(new_values, try_float=False)
-        res_values = maybe_cast_pointwise_result(npvalues, self.dtype, same_dtype=False)
+        # same_dtype here is a kludge to avoid casting e.g. [True, False] to
+        #  ["True", "False"]
+        same_dtype = isinstance(self.dtype, (StringDtype, CategoricalDtype))
+        res_values = maybe_cast_pointwise_result(
+            npvalues, self.dtype, same_dtype=same_dtype
+        )
         return self._constructor(res_values, index=new_index, name=new_name, copy=False)
 
     def combine_first(self, other) -> Series:

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)