DEPR: disallow int fill_value in shift with dt64/td64

doc/source/whatsnew/v2.0.0.rst

@@ -243,6 +243,7 @@ Removal of prior version deprecations/changes
 - Removed :meth:`Series.str.__iter__` (:issue:`28277`)
 - Removed ``pandas.SparseArray`` in favor of :class:`arrays.SparseArray` (:issue:`30642`)
 - Removed ``pandas.SparseSeries`` and ``pandas.SparseDataFrame``, including pickle support. (:issue:`30642`)
+- Enforced disallowing passing an integer ``fill_value`` to :meth:`DataFrame.shift` and :meth:`Series.shift`` with datetime64, timedelta64, or period dtypes (:issue:`32591`)
 - Enforced disallowing a string column label into ``times`` in :meth:`DataFrame.ewm` (:issue:`43265`)
 - Enforced disallowing a tuple of column labels into :meth:`.DataFrameGroupBy.__getitem__` (:issue:`30546`)
 - Removed setting Categorical._codes directly (:issue:`41429`)

pandas/core/arrays/_mixins.py

@@ -252,16 +252,11 @@ def _validate_searchsorted_value(
     @doc(ExtensionArray.shift)
     def shift(self, periods: int = 1, fill_value=None, axis: AxisInt = 0):
 
-        fill_value = self._validate_shift_value(fill_value)
+        fill_value = self._validate_scalar(fill_value)
         new_values = shift(self._ndarray, periods, axis, fill_value)
 
         return self._from_backing_data(new_values)
 
-    def _validate_shift_value(self, fill_value):
-        # TODO(2.0): after deprecation in datetimelikearraymixin is enforced,
-        #  we can remove this and use validate_fill_value directly
-        return self._validate_scalar(fill_value)
-
     def __setitem__(self, key, value) -> None:
         key = check_array_indexer(self, key)
         value = self._validate_setitem_value(value)

pandas/core/arrays/datetimelike.py

@@ -603,37 +603,6 @@ def _validate_comparison_value(self, other):
 
         return other
 
-    def _validate_shift_value(self, 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):
-            fill_value = self._scalar_type(fill_value)
-        else:
-            new_fill: DatetimeLikeScalar
-
-            # only warn if we're not going to raise
-            if self._scalar_type is Period and lib.is_integer(fill_value):
-                # kludge for #31971 since Period(integer) tries to cast to str
-                new_fill = Period._from_ordinal(fill_value, freq=self.freq)
-            else:
-                new_fill = self._scalar_type(fill_value)
-
-            # stacklevel here is chosen to be correct when called from
-            #  DataFrame.shift or Series.shift
-            warnings.warn(
-                f"Passing {type(fill_value)} to shift is deprecated and "
-                "will raise in a future version, pass "
-                f"{self._scalar_type.__name__} instead.",
-                FutureWarning,
-                # There is no way to hard-code the level since this might be
-                #  reached directly or called from the Index or Block method
-                stacklevel=find_stack_level(),
-            )
-            fill_value = new_fill
-
-        return self._unbox(fill_value, setitem=True)
-
     def _validate_scalar(
         self,
         value,

pandas/tests/arrays/test_datetimelike.py

@@ -554,22 +554,12 @@ def test_inplace_arithmetic(self):
         tm.assert_equal(arr, expected)
 
     def test_shift_fill_int_deprecated(self):
-        # GH#31971
+        # GH#31971, enforced in 2.0
         data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9
         arr = self.array_cls(data, freq="D")
 
-        msg = "Passing <class 'int'> to shift"
-        with tm.assert_produces_warning(FutureWarning, match=msg):
-            result = arr.shift(1, fill_value=1)
-
-        expected = arr.copy()
-        if self.array_cls is PeriodArray:
-            fill_val = arr._scalar_type._from_ordinal(1, freq=arr.freq)
-        else:
-            fill_val = arr._scalar_type(1)
-        expected[0] = fill_val
-        expected[1:] = arr[:-1]
-        tm.assert_equal(result, expected)
+        with pytest.raises(TypeError, match="value should be a"):
+            arr.shift(1, fill_value=1)
 
     def test_median(self, arr1d):
         arr = arr1d

pandas/tests/frame/methods/test_shift.py

@@ -505,26 +505,19 @@ def test_shift_dt64values_int_fill_deprecated(self):
         # GH#31971
         ser = Series([pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-02")])
 
-        with tm.assert_produces_warning(FutureWarning):
-            result = ser.shift(1, fill_value=0)
-        expected = Series([pd.Timestamp(0), ser[0]])
-        tm.assert_series_equal(result, expected)
+        with pytest.raises(TypeError, match="value should be a"):
+            ser.shift(1, fill_value=0)
 
         df = ser.to_frame()
-        with tm.assert_produces_warning(FutureWarning):
-            result = df.shift(1, fill_value=0)
-        expected = expected.to_frame()
-        tm.assert_frame_equal(result, expected)
+        with pytest.raises(TypeError, match="value should be a"):
+            df.shift(1, fill_value=0)
 
         # axis = 1
         df2 = DataFrame({"A": ser, "B": ser})
         df2._consolidate_inplace()
 
-        with tm.assert_produces_warning(FutureWarning):
-            result = df2.shift(1, axis=1, fill_value=0)
-
-        expected = DataFrame({"A": [pd.Timestamp(0), pd.Timestamp(0)], "B": df2["A"]})
-        tm.assert_frame_equal(result, expected)
+        with pytest.raises(TypeError, match="value should be a"):
+            df2.shift(1, axis=1, fill_value=0)
 
         # same thing but not consolidated
         # This isn't great that we get different behavior, but