REF: use array_algos shift for Categorical.shift

pandas/core/array_algos/transforms.py

@@ -10,6 +10,10 @@
 def shift(values: np.ndarray, periods: int, axis: int, fill_value) -> np.ndarray:
     new_values = values
 
+    if periods == 0:
+        # TODO: should we copy here?
+        return new_values
+
     # make sure array sent to np.roll is c_contiguous
     f_ordered = values.flags.f_contiguous
     if f_ordered:

pandas/core/arrays/categorical.py

@@ -27,7 +27,6 @@
 from pandas.core.dtypes.common import (
     ensure_int64,
     ensure_object,
-    ensure_platform_int,
     is_categorical_dtype,
     is_datetime64_dtype,
     is_dict_like,
@@ -51,6 +50,7 @@
 from pandas.core.accessor import PandasDelegate, delegate_names
 import pandas.core.algorithms as algorithms
 from pandas.core.algorithms import _get_data_algo, factorize, take, take_1d, unique1d
+from pandas.core.array_algos.transforms import shift
 from pandas.core.arrays.base import ExtensionArray, _extension_array_shared_docs
 from pandas.core.base import NoNewAttributesMixin, PandasObject, _shared_docs
 import pandas.core.common as com
@@ -1241,23 +1241,41 @@ def shift(self, periods, fill_value=None):
         codes = self.codes
         if codes.ndim > 1:
             raise NotImplementedError("Categorical with ndim > 1.")
-        if np.prod(codes.shape) and (periods != 0):
-            codes = np.roll(codes, ensure_platform_int(periods), axis=0)
-            if isna(fill_value):
-                fill_value = -1
-            elif fill_value in self.categories:
-                fill_value = self.categories.get_loc(fill_value)
-            else:
-                raise ValueError(
-                    f"'fill_value={fill_value}' is not present "
-                    "in this Categorical's categories"
-                )
-            if periods > 0:
-                codes[:periods] = fill_value
-            else:
-                codes[periods:] = fill_value
 
-        return self.from_codes(codes, dtype=self.dtype)
+        fill_value = self._validate_fill_value(fill_value)
+
+        codes = shift(codes.copy(), periods, axis=0, fill_value=fill_value)
+
+        return self._constructor(codes, dtype=self.dtype, fastpath=True)
+
+    def _validate_fill_value(self, fill_value):
+        """
+        Convert a user-facing fill_value to  a representation to use with our
+        underlying ndarray, raising ValueError if this is not possible.
+
+        Parameters
+        ----------
+        fill_value : object
+
+        Returns
+        -------
+        fill_value : int
+
+        Raises
+        ------
+        ValueError
+        """
+
+        if isna(fill_value):
+            fill_value = -1
+        elif fill_value in self.categories:
+            fill_value = self.categories.get_loc(fill_value)
+        else:
+            raise ValueError(
+                f"'fill_value={fill_value}' is not present "
+                "in this Categorical's categories"
+            )
+        return fill_value
 
     def __array__(self, dtype=None) -> np.ndarray:
         """
@@ -1835,24 +1853,12 @@ def take(self, indexer, allow_fill: bool = False, fill_value=None):
         """
         indexer = np.asarray(indexer, dtype=np.intp)
 
-        dtype = self.dtype
-
-        if isna(fill_value):
-            fill_value = -1
-        elif allow_fill:
+        if allow_fill:
             # convert user-provided `fill_value` to codes
-            if fill_value in self.categories:
-                fill_value = self.categories.get_loc(fill_value)
-            else:
-                msg = (
-                    f"'fill_value' ('{fill_value}') is not in this "
-                    "Categorical's categories."
-                )
-                raise TypeError(msg)
+            fill_value = self._validate_fill_value(fill_value)
 
         codes = take(self._codes, indexer, allow_fill=allow_fill, fill_value=fill_value)
-        result = type(self).from_codes(codes, dtype=dtype)
-        return result
+        return self._constructor(codes, dtype=self.dtype, fastpath=True)
 
     def take_nd(self, indexer, allow_fill: bool = False, fill_value=None):
         # GH#27745 deprecate alias that other EAs dont have

pandas/core/arrays/datetimelike.py

@@ -769,6 +769,7 @@ def shift(self, periods=1, fill_value=None, axis=0):
         if not self.size or periods == 0:
             return self.copy()
 
+        # TODO(2.0): once this deprecation is enforced, used _validate_fill_value
         if is_valid_nat_for_dtype(fill_value, self.dtype):
             fill_value = NaT
         elif not isinstance(fill_value, self._recognized_scalars):

pandas/tests/arrays/categorical/test_algos.py

@@ -184,8 +184,8 @@ def test_take_fill_value(self):
     def test_take_fill_value_new_raises(self):
         # https://github.com/pandas-dev/pandas/issues/23296
         cat = pd.Categorical(["a", "b", "c"])
-        xpr = r"'fill_value' \('d'\) is not in this Categorical's categories."
-        with pytest.raises(TypeError, match=xpr):
+        xpr = r"'fill_value=d' is not present in this Categorical's categories"
+        with pytest.raises(ValueError, match=xpr):
             cat.take([0, 1, -1], fill_value="d", allow_fill=True)
 
     def test_take_nd_deprecated(self):

pandas/tests/frame/test_reshape.py

@@ -320,9 +320,9 @@ def test_unstack_fill_frame_categorical(self):
         )
         tm.assert_frame_equal(result, expected)
 
-        # Fill with non-category results in a TypeError
-        msg = r"'fill_value' \('d'\) is not in"
-        with pytest.raises(TypeError, match=msg):
+        # Fill with non-category results in a ValueError
+        msg = r"'fill_value=d' is not present in"
+        with pytest.raises(ValueError, match=msg):
             data.unstack(fill_value="d")
 
         # Fill with category value replaces missing values as expected