diff --git a/pandas/core/window/rolling.py b/pandas/core/window/rolling.py
index f516871f789d0..f7e81f41b8675 100644
--- a/pandas/core/window/rolling.py
+++ b/pandas/core/window/rolling.py
@@ -6,13 +6,23 @@
 from functools import partial
 import inspect
 from textwrap import dedent
-from typing import Callable, Dict, List, Optional, Set, Tuple, Type, Union
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Dict,
+    List,
+    Optional,
+    Set,
+    Tuple,
+    Type,
+    Union,
+)
 
 import numpy as np
 
 from pandas._libs.tslibs import BaseOffset, to_offset
 import pandas._libs.window.aggregations as window_aggregations
-from pandas._typing import ArrayLike, Axis, FrameOrSeries, Label
+from pandas._typing import ArrayLike, Axis, FrameOrSeriesUnion, Label
 from pandas.compat._optional import import_optional_dependency
 from pandas.compat.numpy import function as nv
 from pandas.util._decorators import Appender, Substitution, cache_readonly, doc
@@ -55,6 +65,9 @@
 )
 from pandas.core.window.numba_ import generate_numba_apply_func
 
+if TYPE_CHECKING:
+    from pandas import Series
+
 
 def calculate_center_offset(window) -> int:
     """
@@ -145,7 +158,7 @@ class _Window(PandasObject, ShallowMixin, SelectionMixin):
 
     def __init__(
         self,
-        obj: FrameOrSeries,
+        obj: FrameOrSeriesUnion,
         window=None,
         min_periods: Optional[int] = None,
         center: bool = False,
@@ -219,7 +232,7 @@ def _validate_get_window_bounds_signature(window: BaseIndexer) -> None:
                 f"get_window_bounds"
             )
 
-    def _create_blocks(self, obj: FrameOrSeries):
+    def _create_blocks(self, obj: FrameOrSeriesUnion):
         """
         Split data into blocks & return conformed data.
         """
@@ -381,7 +394,7 @@ def _wrap_result(self, result, block=None, obj=None):
             return type(obj)(result, index=index, columns=block.columns)
         return result
 
-    def _wrap_results(self, results, obj, skipped: List[int]) -> FrameOrSeries:
+    def _wrap_results(self, results, obj, skipped: List[int]) -> FrameOrSeriesUnion:
         """
         Wrap the results.
 
@@ -394,22 +407,23 @@ def _wrap_results(self, results, obj, skipped: List[int]) -> FrameOrSeries:
         """
         from pandas import Series, concat
 
+        if obj.ndim == 1:
+            if not results:
+                raise DataError("No numeric types to aggregate")
+            assert len(results) == 1
+            return Series(results[0], index=obj.index, name=obj.name)
+
         exclude: List[Label] = []
-        if obj.ndim == 2:
-            orig_blocks = list(obj._to_dict_of_blocks(copy=False).values())
-            for i in skipped:
-                exclude.extend(orig_blocks[i].columns)
-        else:
-            orig_blocks = [obj]
+        orig_blocks = list(obj._to_dict_of_blocks(copy=False).values())
+        for i in skipped:
+            exclude.extend(orig_blocks[i].columns)
 
         kept_blocks = [blk for i, blk in enumerate(orig_blocks) if i not in skipped]
 
         final = []
         for result, block in zip(results, kept_blocks):
 
-            result = self._wrap_result(result, block=block, obj=obj)
-            if result.ndim == 1:
-                return result
+            result = type(obj)(result, index=obj.index, columns=block.columns)
             final.append(result)
 
         exclude = exclude or []
@@ -488,13 +502,31 @@ def _get_window_indexer(self, window: int) -> BaseIndexer:
             return VariableWindowIndexer(index_array=self._on.asi8, window_size=window)
         return FixedWindowIndexer(window_size=window)
 
+    def _apply_series(self, homogeneous_func: Callable[..., ArrayLike]) -> "Series":
+        """
+        Series version of _apply_blockwise
+        """
+        _, obj = self._create_blocks(self._selected_obj)
+        values = obj.values
+
+        try:
+            values = self._prep_values(obj.values)
+        except (TypeError, NotImplementedError) as err:
+            raise DataError("No numeric types to aggregate") from err
+
+        result = homogeneous_func(values)
+        return obj._constructor(result, index=obj.index, name=obj.name)
+
     def _apply_blockwise(
         self, homogeneous_func: Callable[..., ArrayLike]
-    ) -> FrameOrSeries:
+    ) -> FrameOrSeriesUnion:
         """
         Apply the given function to the DataFrame broken down into homogeneous
         sub-frames.
         """
+        if self._selected_obj.ndim == 1:
+            return self._apply_series(homogeneous_func)
+
         # This isn't quite blockwise, since `blocks` is actually a collection
         #  of homogenenous DataFrames.
         blocks, obj = self._create_blocks(self._selected_obj)
@@ -505,12 +537,9 @@ def _apply_blockwise(
             try:
                 values = self._prep_values(b.values)
 
-            except (TypeError, NotImplementedError) as err:
-                if isinstance(obj, ABCDataFrame):
-                    skipped.append(i)
-                    continue
-                else:
-                    raise DataError("No numeric types to aggregate") from err
+            except (TypeError, NotImplementedError):
+                skipped.append(i)
+                continue
 
             result = homogeneous_func(values)
             results.append(result)
@@ -2234,7 +2263,7 @@ def _apply(
     def _constructor(self):
         return Rolling
 
-    def _create_blocks(self, obj: FrameOrSeries):
+    def _create_blocks(self, obj: FrameOrSeriesUnion):
         """
         Split data into blocks & return conformed data.
         """
@@ -2275,7 +2304,7 @@ def _get_window_indexer(self, window: int) -> GroupbyRollingIndexer:
         if isinstance(self.window, BaseIndexer):
             rolling_indexer = type(self.window)
             indexer_kwargs = self.window.__dict__
-            assert isinstance(indexer_kwargs, dict)
+            assert isinstance(indexer_kwargs, dict)  # for mypy
             # We'll be using the index of each group later
             indexer_kwargs.pop("index_array", None)
         elif self.is_freq_type: