REF: de-duplicate _validate_fill_value/_validate_scalar

pandas/core/arrays/_mixins.py

@@ -94,7 +94,7 @@ def take(
         axis: int = 0,
     ) -> NDArrayBackedExtensionArrayT:
         if allow_fill:
-            fill_value = self._validate_fill_value(fill_value)
+            fill_value = self._validate_scalar(fill_value)
 
         new_data = take(
             self._ndarray,
@@ -107,25 +107,6 @@ def take(
         )
         return self._from_backing_data(new_data)
 
-    def _validate_fill_value(self, fill_value):
-        """
-        If a fill_value is passed to `take` convert it to a representation
-        suitable for self._ndarray, raising TypeError if this is not possible.
-
-        Parameters
-        ----------
-        fill_value : object
-
-        Returns
-        -------
-        fill_value : native representation
-
-        Raises
-        ------
-        TypeError
-        """
-        raise AbstractMethodError(self)
-
     # ------------------------------------------------------------------------
 
     def equals(self, other) -> bool:
@@ -194,7 +175,7 @@ def shift(self, periods=1, fill_value=None, axis=0):
     def _validate_shift_value(self, fill_value):
         # TODO: after deprecation in datetimelikearraymixin is enforced,
         #  we can remove this and ust validate_fill_value directly
-        return self._validate_fill_value(fill_value)
+        return self._validate_scalar(fill_value)
 
     def __setitem__(self, key, value):
         key = check_array_indexer(self, key)

pandas/core/arrays/categorical.py

@@ -1407,7 +1407,7 @@ def _validate_searchsorted_value(self, value):
             codes = np.array(locs, dtype=self.codes.dtype)  # type: ignore[assignment]
         return codes
 
-    def _validate_fill_value(self, fill_value):
+    def _validate_scalar(self, fill_value):
         """
         Convert a user-facing fill_value to a representation to use with our
         underlying ndarray, raising TypeError if this is not possible.
@@ -1436,8 +1436,6 @@ def _validate_fill_value(self, fill_value):
             )
         return fill_value
 
-    _validate_scalar = _validate_fill_value
-
     # -------------------------------------------------------------
 
     def __array__(self, dtype: NpDtype | None = None) -> np.ndarray:

pandas/core/arrays/datetimelike.py

@@ -557,27 +557,8 @@ def _validate_comparison_value(self, other):
 
         return other
 
-    def _validate_fill_value(self, fill_value):
-        """
-        If a fill_value is passed to `take` convert it to an i8 representation,
-        raising TypeError if this is not possible.
-
-        Parameters
-        ----------
-        fill_value : object
-
-        Returns
-        -------
-        fill_value : np.int64, np.datetime64, or np.timedelta64
-
-        Raises
-        ------
-        TypeError
-        """
-        return self._validate_scalar(fill_value)
-
     def _validate_shift_value(self, fill_value):
-        # TODO(2.0): once this deprecation is enforced, use _validate_fill_value
+        # TODO(2.0): once this deprecation is enforced, use _validate_scalar
         if is_valid_na_for_dtype(fill_value, self.dtype):
             fill_value = NaT
         elif isinstance(fill_value, self._recognized_scalars):

pandas/core/arrays/interval.py

@@ -801,7 +801,7 @@ def fillna(
         if limit is not None:
             raise TypeError("limit is not supported for IntervalArray.")
 
-        value_left, value_right = self._validate_fill_value(value)
+        value_left, value_right = self._validate_scalar(value)
 
         left = self.left.fillna(value=value_left)
         right = self.right.fillna(value=value_right)
@@ -1000,7 +1000,7 @@ def take(
 
         fill_left = fill_right = fill_value
         if allow_fill:
-            fill_left, fill_right = self._validate_fill_value(fill_value)
+            fill_left, fill_right = self._validate_scalar(fill_value)
 
         left_take = take(
             self._left, indices, allow_fill=allow_fill, fill_value=fill_left
@@ -1037,6 +1037,7 @@ def _validate_scalar(self, value):
         if isinstance(value, Interval):
             self._check_closed_matches(value, name="value")
             left, right = value.left, value.right
+            # TODO: check subdtype match like _validate_setitem_value?
         elif is_valid_na_for_dtype(value, self.left.dtype):
             # GH#18295
             left = right = value
@@ -1046,9 +1047,6 @@ def _validate_scalar(self, value):
             )
         return left, right
 
-    def _validate_fill_value(self, value):
-        return self._validate_scalar(value)
-
     def _validate_setitem_value(self, value):
         needs_float_conversion = False
 

pandas/core/arrays/numpy_.py

@@ -190,7 +190,7 @@ def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
     def isna(self) -> np.ndarray:
         return isna(self._ndarray)
 
-    def _validate_fill_value(self, fill_value):
+    def _validate_scalar(self, fill_value):
         if fill_value is None:
             # Primarily for subclasses
             fill_value = self.dtype.na_value