diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 88d18e3e230c6..08a5b34167552 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -150,9 +150,15 @@ repos:
             (?x)
             \#\ type:\ (?!ignore)|
             \#\ type:\s?ignore(?!\[)|
-            \)\ ->\ \"
+
+            # string return type annotation
+            \)\ ->\ \"|
+
+            # string type annotation in generic
+            (Callable|Dict|Iterable|Iterator|List|Mapping|MutableMapping|Sequence|Set|Tuple|Type|Union)\[[^\]]*\".+?\"[^\]]*\]
         language: pygrep
         types: [python]
+        exclude: ^pandas/_typing\.py$
     -   id: np-bool
         name: Check for use of np.bool instead of np.bool_
         entry: np\.bool[^_8]
diff --git a/pandas/core/aggregation.py b/pandas/core/aggregation.py
index 2145551833e90..bf9fdbf82dd0e 100644
--- a/pandas/core/aggregation.py
+++ b/pandas/core/aggregation.py
@@ -3,6 +3,8 @@
 kwarg aggregations in groupby and DataFrame/Series aggregation
 """
 
+from __future__ import annotations
+
 from collections import defaultdict
 from functools import partial
 from typing import (
@@ -296,7 +298,7 @@ def relabel_result(
     func: Dict[str, List[Union[Callable, str]]],
     columns: Iterable[Hashable],
     order: Iterable[int],
-) -> Dict[Hashable, "Series"]:
+) -> Dict[Hashable, Series]:
     """
     Internal function to reorder result if relabelling is True for
     dataframe.agg, and return the reordered result in dict.
@@ -323,7 +325,7 @@ def relabel_result(
     reordered_indexes = [
         pair[0] for pair in sorted(zip(columns, order), key=lambda t: t[1])
     ]
-    reordered_result_in_dict: Dict[Hashable, "Series"] = {}
+    reordered_result_in_dict: Dict[Hashable, Series] = {}
     idx = 0
 
     reorder_mask = not isinstance(result, ABCSeries) and len(result.columns) > 1
diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index 085ad5e6a0dcf..902ffe82c1062 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -567,7 +567,7 @@ def factorize(
     sort: bool = False,
     na_sentinel: Optional[int] = -1,
     size_hint: Optional[int] = None,
-) -> Tuple[np.ndarray, Union[np.ndarray, "Index"]]:
+) -> Tuple[np.ndarray, Union[np.ndarray, Index]]:
     """
     Encode the object as an enumerated type or categorical variable.
 
diff --git a/pandas/core/arrays/boolean.py b/pandas/core/arrays/boolean.py
index 2cb8d58c7ec39..dd281a39907fd 100644
--- a/pandas/core/arrays/boolean.py
+++ b/pandas/core/arrays/boolean.py
@@ -72,7 +72,7 @@ def numpy_dtype(self) -> np.dtype:
         return np.dtype("bool")
 
     @classmethod
-    def construct_array_type(cls) -> Type["BooleanArray"]:
+    def construct_array_type(cls) -> Type[BooleanArray]:
         """
         Return the array type associated with this dtype.
 
@@ -94,7 +94,7 @@ def _is_numeric(self) -> bool:
         return True
 
     def __from_arrow__(
-        self, array: Union["pyarrow.Array", "pyarrow.ChunkedArray"]
+        self, array: Union[pyarrow.Array, pyarrow.ChunkedArray]
     ) -> BooleanArray:
         """
         Construct BooleanArray from pyarrow Array/ChunkedArray.
diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index 2c04d5f4d45c6..f3d3d61fc0ca1 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -422,7 +422,7 @@ def dtype(self) -> CategoricalDtype:
         return self._dtype
 
     @property
-    def _constructor(self) -> Type["Categorical"]:
+    def _constructor(self) -> Type[Categorical]:
         return Categorical
 
     @classmethod
diff --git a/pandas/core/arrays/floating.py b/pandas/core/arrays/floating.py
index dc46cf9e3cf68..2c3b3d3c2f0b4 100644
--- a/pandas/core/arrays/floating.py
+++ b/pandas/core/arrays/floating.py
@@ -47,7 +47,7 @@ def _is_numeric(self) -> bool:
         return True
 
     @classmethod
-    def construct_array_type(cls) -> Type["FloatingArray"]:
+    def construct_array_type(cls) -> Type[FloatingArray]:
         """
         Return the array type associated with this dtype.
 
diff --git a/pandas/core/arrays/integer.py b/pandas/core/arrays/integer.py
index f128d2ee6c92f..ff1af80f81ac6 100644
--- a/pandas/core/arrays/integer.py
+++ b/pandas/core/arrays/integer.py
@@ -57,7 +57,7 @@ def _is_numeric(self) -> bool:
         return True
 
     @classmethod
-    def construct_array_type(cls) -> Type["IntegerArray"]:
+    def construct_array_type(cls) -> Type[IntegerArray]:
         """
         Return the array type associated with this dtype.
 
diff --git a/pandas/core/arrays/numeric.py b/pandas/core/arrays/numeric.py
index ed9e37bd68184..49f0d7e66c005 100644
--- a/pandas/core/arrays/numeric.py
+++ b/pandas/core/arrays/numeric.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import datetime
 import numbers
 from typing import TYPE_CHECKING, Any, List, Union
@@ -25,7 +27,7 @@
 
 class NumericDtype(BaseMaskedDtype):
     def __from_arrow__(
-        self, array: Union["pyarrow.Array", "pyarrow.ChunkedArray"]
+        self, array: Union[pyarrow.Array, pyarrow.ChunkedArray]
     ) -> BaseMaskedArray:
         """
         Construct IntegerArray/FloatingArray from pyarrow Array/ChunkedArray.
diff --git a/pandas/core/arrays/numpy_.py b/pandas/core/arrays/numpy_.py
index 85dffb1113d35..787f89ab15679 100644
--- a/pandas/core/arrays/numpy_.py
+++ b/pandas/core/arrays/numpy_.py
@@ -89,7 +89,7 @@ def construct_from_string(cls, string: str) -> PandasDtype:
         return cls(dtype)
 
     @classmethod
-    def construct_array_type(cls) -> Type["PandasArray"]:
+    def construct_array_type(cls) -> Type[PandasArray]:
         """
         Return the array type associated with this dtype.
 
@@ -155,7 +155,7 @@ class PandasArray(
     # ------------------------------------------------------------------------
     # Constructors
 
-    def __init__(self, values: Union[np.ndarray, "PandasArray"], copy: bool = False):
+    def __init__(self, values: Union[np.ndarray, PandasArray], copy: bool = False):
         if isinstance(values, type(self)):
             values = values._ndarray
         if not isinstance(values, np.ndarray):
diff --git a/pandas/core/arrays/period.py b/pandas/core/arrays/period.py
index 749ec0a2b8848..e2d565eb6edf1 100644
--- a/pandas/core/arrays/period.py
+++ b/pandas/core/arrays/period.py
@@ -201,7 +201,7 @@ def _simple_new(
 
     @classmethod
     def _from_sequence(
-        cls: Type["PeriodArray"],
+        cls: Type[PeriodArray],
         scalars: Union[Sequence[Optional[Period]], AnyArrayLike],
         *,
         dtype: Optional[Dtype] = None,
diff --git a/pandas/core/arrays/sparse/array.py b/pandas/core/arrays/sparse/array.py
index 4cae2e48c84c8..62d6ca745b992 100644
--- a/pandas/core/arrays/sparse/array.py
+++ b/pandas/core/arrays/sparse/array.py
@@ -896,7 +896,7 @@ def _take_with_fill(self, indices, fill_value=None) -> np.ndarray:
 
         return taken
 
-    def _take_without_fill(self, indices) -> Union[np.ndarray, "SparseArray"]:
+    def _take_without_fill(self, indices) -> Union[np.ndarray, SparseArray]:
         to_shift = indices < 0
         indices = indices.copy()
 
diff --git a/pandas/core/arrays/sparse/dtype.py b/pandas/core/arrays/sparse/dtype.py
index 4e4c8f1aad671..4c1c1b42ff6fa 100644
--- a/pandas/core/arrays/sparse/dtype.py
+++ b/pandas/core/arrays/sparse/dtype.py
@@ -173,7 +173,7 @@ def __repr__(self) -> str:
         return self.name
 
     @classmethod
-    def construct_array_type(cls) -> Type["SparseArray"]:
+    def construct_array_type(cls) -> Type[SparseArray]:
         """
         Return the array type associated with this dtype.
 
diff --git a/pandas/core/arrays/string_.py b/pandas/core/arrays/string_.py
index 3234d36b3dbe7..2e4580207bc8a 100644
--- a/pandas/core/arrays/string_.py
+++ b/pandas/core/arrays/string_.py
@@ -71,7 +71,7 @@ def type(self) -> Type[str]:
         return str
 
     @classmethod
-    def construct_array_type(cls) -> Type["StringArray"]:
+    def construct_array_type(cls) -> Type[StringArray]:
         """
         Return the array type associated with this dtype.
 
@@ -85,7 +85,7 @@ def __repr__(self) -> str:
         return "StringDtype"
 
     def __from_arrow__(
-        self, array: Union["pyarrow.Array", "pyarrow.ChunkedArray"]
+        self, array: Union[pyarrow.Array, pyarrow.ChunkedArray]
     ) -> StringArray:
         """
         Construct StringArray from pyarrow Array/ChunkedArray.
diff --git a/pandas/core/arrays/string_arrow.py b/pandas/core/arrays/string_arrow.py
index 4c073883abf89..cdca67237698b 100644
--- a/pandas/core/arrays/string_arrow.py
+++ b/pandas/core/arrays/string_arrow.py
@@ -87,7 +87,7 @@ def type(self) -> Type[str]:
         return str
 
     @classmethod
-    def construct_array_type(cls) -> Type["ArrowStringArray"]:
+    def construct_array_type(cls) -> Type[ArrowStringArray]:
         """
         Return the array type associated with this dtype.
 
@@ -104,7 +104,7 @@ def __repr__(self) -> str:
         return "ArrowStringDtype"
 
     def __from_arrow__(
-        self, array: Union["pa.Array", "pa.ChunkedArray"]
+        self, array: Union[pa.Array, pa.ChunkedArray]
     ) -> ArrowStringArray:
         """
         Construct StringArray from pyarrow Array/ChunkedArray.
diff --git a/pandas/core/computation/ops.py b/pandas/core/computation/ops.py
index 74bee80c6c8a6..7b42b21cadc1f 100644
--- a/pandas/core/computation/ops.py
+++ b/pandas/core/computation/ops.py
@@ -2,6 +2,8 @@
 Operator classes for eval.
 """
 
+from __future__ import annotations
+
 from datetime import datetime
 from distutils.version import LooseVersion
 from functools import partial
@@ -203,7 +205,7 @@ class Op:
 
     op: str
 
-    def __init__(self, op: str, operands: Iterable[Union[Term, "Op"]], encoding=None):
+    def __init__(self, op: str, operands: Iterable[Union[Term, Op]], encoding=None):
         self.op = _bool_op_map.get(op, op)
         self.operands = operands
         self.encoding = encoding
diff --git a/pandas/core/describe.py b/pandas/core/describe.py
index 22de5ae1e082f..fa3fbc865c5bd 100644
--- a/pandas/core/describe.py
+++ b/pandas/core/describe.py
@@ -164,7 +164,7 @@ def describe_frame(
     return d
 
 
-def reorder_columns(ldesc: Sequence["Series"]) -> List[Hashable]:
+def reorder_columns(ldesc: Sequence[Series]) -> List[Hashable]:
     """Set a convenient order for rows for display."""
     names: List[Hashable] = []
     ldesc_indexes = sorted((x.index for x in ldesc), key=len)
diff --git a/pandas/core/dtypes/base.py b/pandas/core/dtypes/base.py
index 6adb4984d156e..0c0c56ff39280 100644
--- a/pandas/core/dtypes/base.py
+++ b/pandas/core/dtypes/base.py
@@ -2,6 +2,8 @@
 Extend pandas with custom array types.
 """
 
+from __future__ import annotations
+
 from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Type, Union
 
 import numpy as np
@@ -186,7 +188,7 @@ def names(self) -> Optional[List[str]]:
         return None
 
     @classmethod
-    def construct_array_type(cls) -> Type["ExtensionArray"]:
+    def construct_array_type(cls) -> Type[ExtensionArray]:
         """
         Return the array type associated with this dtype.
 
diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index 0941967ef6bee..90513cf0550b2 100644
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -397,7 +397,7 @@ def maybe_cast_result_dtype(dtype: DtypeObj, how: str) -> DtypeObj:
 
 
 def maybe_cast_to_extension_array(
-    cls: Type["ExtensionArray"], obj: ArrayLike, dtype: Optional[ExtensionDtype] = None
+    cls: Type[ExtensionArray], obj: ArrayLike, dtype: Optional[ExtensionDtype] = None
 ) -> ArrayLike:
     """
     Call to `_from_sequence` that returns the object unchanged on Exception.
diff --git a/pandas/core/dtypes/dtypes.py b/pandas/core/dtypes/dtypes.py
index cefab33976ba8..ac02abc64cfa4 100644
--- a/pandas/core/dtypes/dtypes.py
+++ b/pandas/core/dtypes/dtypes.py
@@ -59,7 +59,7 @@ class PandasExtensionDtype(ExtensionDtype):
     base = None
     isbuiltin = 0
     isnative = 0
-    _cache: Dict[str_type, "PandasExtensionDtype"] = {}
+    _cache: Dict[str_type, PandasExtensionDtype] = {}
 
     def __str__(self) -> str_type:
         """
@@ -445,7 +445,7 @@ def _hash_categories(categories, ordered: Ordered = True) -> int:
         return np.bitwise_xor.reduce(hashed)
 
     @classmethod
-    def construct_array_type(cls) -> Type["Categorical"]:
+    def construct_array_type(cls) -> Type[Categorical]:
         """
         Return the array type associated with this dtype.
 
@@ -514,7 +514,7 @@ def validate_categories(categories, fastpath: bool = False):
         return categories
 
     def update_dtype(
-        self, dtype: Union[str_type, "CategoricalDtype"]
+        self, dtype: Union[str_type, CategoricalDtype]
     ) -> CategoricalDtype:
         """
         Returns a CategoricalDtype with categories and ordered taken from dtype
@@ -647,7 +647,7 @@ class DatetimeTZDtype(PandasExtensionDtype):
     _match = re.compile(r"(datetime64|M8)\[(?P<unit>.+), (?P<tz>.+)\]")
     _cache: Dict[str_type, PandasExtensionDtype] = {}
 
-    def __init__(self, unit: Union[str_type, "DatetimeTZDtype"] = "ns", tz=None):
+    def __init__(self, unit: Union[str_type, DatetimeTZDtype] = "ns", tz=None):
         if isinstance(unit, DatetimeTZDtype):
             # error: "str" has no attribute "tz"
             unit, tz = unit.unit, unit.tz  # type: ignore[attr-defined]
@@ -694,7 +694,7 @@ def tz(self):
         return self._tz
 
     @classmethod
-    def construct_array_type(cls) -> Type["DatetimeArray"]:
+    def construct_array_type(cls) -> Type[DatetimeArray]:
         """
         Return the array type associated with this dtype.
 
@@ -940,7 +940,7 @@ def is_dtype(cls, dtype: object) -> bool:
         return super().is_dtype(dtype)
 
     @classmethod
-    def construct_array_type(cls) -> Type["PeriodArray"]:
+    def construct_array_type(cls) -> Type[PeriodArray]:
         """
         Return the array type associated with this dtype.
 
@@ -953,7 +953,7 @@ def construct_array_type(cls) -> Type["PeriodArray"]:
         return PeriodArray
 
     def __from_arrow__(
-        self, array: Union["pyarrow.Array", "pyarrow.ChunkedArray"]
+        self, array: Union[pyarrow.Array, pyarrow.ChunkedArray]
     ) -> PeriodArray:
         """
         Construct PeriodArray from pyarrow Array/ChunkedArray.
@@ -1090,7 +1090,7 @@ def subtype(self):
         return self._subtype
 
     @classmethod
-    def construct_array_type(cls) -> Type["IntervalArray"]:
+    def construct_array_type(cls) -> Type[IntervalArray]:
         """
         Return the array type associated with this dtype.
 
@@ -1184,7 +1184,7 @@ def is_dtype(cls, dtype: object) -> bool:
         return super().is_dtype(dtype)
 
     def __from_arrow__(
-        self, array: Union["pyarrow.Array", "pyarrow.ChunkedArray"]
+        self, array: Union[pyarrow.Array, pyarrow.ChunkedArray]
     ) -> IntervalArray:
         """
         Construct IntervalArray from pyarrow Array/ChunkedArray.
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index ffc84ad94459a..c3cfea93d0cc7 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -9440,8 +9440,8 @@ def resample(
         base: Optional[int] = None,
         on=None,
         level=None,
-        origin: Union[str, "TimestampConvertibleTypes"] = "start_day",
-        offset: Optional["TimedeltaConvertibleTypes"] = None,
+        origin: Union[str, TimestampConvertibleTypes] = "start_day",
+        offset: Optional[TimedeltaConvertibleTypes] = None,
     ) -> Resampler:
         return super().resample(
             rule=rule,
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 2bcd5964d3736..50dbfe2596a77 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -1690,7 +1690,7 @@ def _wrap_transformed_output(
 
         return result
 
-    def _wrap_agged_blocks(self, blocks: Sequence["Block"], items: Index) -> DataFrame:
+    def _wrap_agged_blocks(self, blocks: Sequence[Block], items: Index) -> DataFrame:
         if not self.as_index:
             index = np.arange(blocks[0].values.shape[-1])
             mgr = BlockManager(blocks, axes=[items, index])
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 35a3768be7e73..f41a46456b36a 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1371,7 +1371,7 @@ class GroupBy(BaseGroupBy[FrameOrSeries]):
 
     @final
     @property
-    def _obj_1d_constructor(self) -> Type["Series"]:
+    def _obj_1d_constructor(self) -> Type[Series]:
         # GH28330 preserve subclassed Series/DataFrames
         if isinstance(self.obj, DataFrame):
             return self.obj._constructor_sliced
diff --git a/pandas/core/groupby/grouper.py b/pandas/core/groupby/grouper.py
index 00cc762c7c136..c7dc6d021a4c3 100644
--- a/pandas/core/groupby/grouper.py
+++ b/pandas/core/groupby/grouper.py
@@ -2,6 +2,8 @@
 Provide user facing operators for doing the split part of the
 split-apply-combine paradigm.
 """
+from __future__ import annotations
+
 from typing import Dict, Hashable, List, Optional, Set, Tuple
 import warnings
 
@@ -616,7 +618,7 @@ def get_grouper(
     mutated: bool = False,
     validate: bool = True,
     dropna: bool = True,
-) -> Tuple["ops.BaseGrouper", Set[Hashable], FrameOrSeries]:
+) -> Tuple[ops.BaseGrouper, Set[Hashable], FrameOrSeries]:
     """
     Create and return a BaseGrouper, which is an internal
     mapping of how to create the grouper indexers.
diff --git a/pandas/core/groupby/ops.py b/pandas/core/groupby/ops.py
index 517b848742541..1b1406fe9cd0f 100644
--- a/pandas/core/groupby/ops.py
+++ b/pandas/core/groupby/ops.py
@@ -100,7 +100,7 @@ class BaseGrouper:
     def __init__(
         self,
         axis: Index,
-        groupings: Sequence["grouper.Grouping"],
+        groupings: Sequence[grouper.Grouping],
         sort: bool = True,
         group_keys: bool = True,
         mutated: bool = False,
@@ -119,7 +119,7 @@ def __init__(
         self.dropna = dropna
 
     @property
-    def groupings(self) -> List["grouper.Grouping"]:
+    def groupings(self) -> List[grouper.Grouping]:
         return self._groupings
 
     @property
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 0b46b43514d92..9d84d391a07cb 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -3455,7 +3455,7 @@ def _get_nearest_indexer(self, target: "Index", limit, tolerance) -> np.ndarray:
     @final
     def _filter_indexer_tolerance(
         self,
-        target: Union["Index", np.ndarray, ExtensionArray],
+        target: Union[Index, np.ndarray, ExtensionArray],
         indexer: np.ndarray,
         tolerance,
     ) -> np.ndarray:
@@ -4519,7 +4519,7 @@ def append(self, other):
 
         return self._concat(to_concat, name)
 
-    def _concat(self, to_concat: List["Index"], name: Hashable) -> Index:
+    def _concat(self, to_concat: List[Index], name: Hashable) -> Index:
         """
         Concatenate multiple Index objects.
         """
diff --git a/pandas/core/indexes/category.py b/pandas/core/indexes/category.py
index a8a872ff38fb8..3b274920018d9 100644
--- a/pandas/core/indexes/category.py
+++ b/pandas/core/indexes/category.py
@@ -605,7 +605,7 @@ def map(self, mapper):
         mapped = self._values.map(mapper)
         return Index(mapped, name=self.name)
 
-    def _concat(self, to_concat: List["Index"], name: Hashable) -> Index:
+    def _concat(self, to_concat: List[Index], name: Hashable) -> Index:
         # if calling index is category, don't check dtype of others
         try:
             codes = np.concatenate([self._is_dtype_compat(c).codes for c in to_concat])
diff --git a/pandas/core/indexes/datetimes.py b/pandas/core/indexes/datetimes.py
index 7a178a29b2fd6..0df954e054826 100644
--- a/pandas/core/indexes/datetimes.py
+++ b/pandas/core/indexes/datetimes.py
@@ -404,7 +404,7 @@ def union_many(self, others):
             return this.rename(res_name)
         return this
 
-    def _maybe_utc_convert(self, other: Index) -> Tuple["DatetimeIndex", Index]:
+    def _maybe_utc_convert(self, other: Index) -> Tuple[DatetimeIndex, Index]:
         this = self
 
         if isinstance(other, DatetimeIndex):
diff --git a/pandas/core/indexes/range.py b/pandas/core/indexes/range.py
index 5bd406bfdbc55..267654076127b 100644
--- a/pandas/core/indexes/range.py
+++ b/pandas/core/indexes/range.py
@@ -472,7 +472,7 @@ def argsort(self, *args, **kwargs) -> np.ndarray:
 
     def factorize(
         self, sort: bool = False, na_sentinel: Optional[int] = -1
-    ) -> Tuple[np.ndarray, "RangeIndex"]:
+    ) -> Tuple[np.ndarray, RangeIndex]:
         codes = np.arange(len(self), dtype=np.intp)
         uniques = self
         if sort and self.step < 0:
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 1356b9d3b2ca3..28405e6eeacb3 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -370,7 +370,7 @@ def delete(self, loc) -> None:
         self.values = np.delete(self.values, loc, 0)
         self.mgr_locs = self.mgr_locs.delete(loc)
 
-    def apply(self, func, **kwargs) -> List["Block"]:
+    def apply(self, func, **kwargs) -> List[Block]:
         """
         apply the function to my values; return a block if we are not
         one
@@ -380,7 +380,7 @@ def apply(self, func, **kwargs) -> List["Block"]:
 
         return self._split_op_result(result)
 
-    def reduce(self, func, ignore_failures: bool = False) -> List["Block"]:
+    def reduce(self, func, ignore_failures: bool = False) -> List[Block]:
         # We will apply the function and reshape the result into a single-row
         #  Block with the same mgr_locs; squeezing will be done at a higher level
         assert self.ndim == 2
@@ -401,7 +401,7 @@ def reduce(self, func, ignore_failures: bool = False) -> List["Block"]:
         nb = self.make_block(res_values)
         return [nb]
 
-    def _split_op_result(self, result) -> List["Block"]:
+    def _split_op_result(self, result) -> List[Block]:
         # See also: split_and_operate
         if is_extension_array_dtype(result) and result.ndim > 1:
             # TODO(EA2D): unnecessary with 2D EAs
@@ -420,7 +420,7 @@ def _split_op_result(self, result) -> List["Block"]:
 
     def fillna(
         self, value, limit=None, inplace: bool = False, downcast=None
-    ) -> List["Block"]:
+    ) -> List[Block]:
         """
         fillna on the block with the value. If we fail, then convert to
         ObjectBlock and try again
@@ -461,7 +461,7 @@ def f(mask, val, idx):
 
         return self.split_and_operate(None, f, inplace)
 
-    def _split(self) -> List["Block"]:
+    def _split(self) -> List[Block]:
         """
         Split a block into a list of single-column blocks.
         """
@@ -477,7 +477,7 @@ def _split(self) -> List["Block"]:
 
     def split_and_operate(
         self, mask, f, inplace: bool, ignore_failures: bool = False
-    ) -> List["Block"]:
+    ) -> List[Block]:
         """
         split the block per-column, and apply the callable f
         per-column, return a new block for each. Handle
@@ -545,7 +545,7 @@ def make_a_block(nv, ref_loc):
 
         return new_blocks
 
-    def _maybe_downcast(self, blocks: List["Block"], downcast=None) -> List["Block"]:
+    def _maybe_downcast(self, blocks: List[Block], downcast=None) -> List[Block]:
 
         # no need to downcast our float
         # unless indicated
@@ -554,7 +554,7 @@ def _maybe_downcast(self, blocks: List["Block"], downcast=None) -> List["Block"]
 
         return extend_blocks([b.downcast(downcast) for b in blocks])
 
-    def downcast(self, dtypes=None) -> List["Block"]:
+    def downcast(self, dtypes=None) -> List[Block]:
         """ try to downcast each item to the dict of dtypes if present """
         # turn it off completely
         if dtypes is False:
@@ -670,7 +670,7 @@ def convert(
         datetime: bool = True,
         numeric: bool = True,
         timedelta: bool = True,
-    ) -> List["Block"]:
+    ) -> List[Block]:
         """
         attempt to coerce any object types to better types return a copy
         of the block (if copy = True) by definition we are not an ObjectBlock
@@ -728,7 +728,7 @@ def replace(
         value,
         inplace: bool = False,
         regex: bool = False,
-    ) -> List["Block"]:
+    ) -> List[Block]:
         """
         replace the to_replace value with value, possible to create new
         blocks here this is just a call to putmask. regex is not used here.
@@ -773,7 +773,7 @@ def _replace_regex(
         inplace: bool = False,
         convert: bool = True,
         mask=None,
-    ) -> List["Block"]:
+    ) -> List[Block]:
         """
         Replace elements by the given value.
 
@@ -817,7 +817,7 @@ def _replace_list(
         dest_list: List[Any],
         inplace: bool = False,
         regex: bool = False,
-    ) -> List["Block"]:
+    ) -> List[Block]:
         """
         See BlockManager._replace_list docstring.
         """
@@ -854,7 +854,7 @@ def comp(s: Scalar, mask: np.ndarray, regex: bool = False) -> np.ndarray:
 
         rb = [self if inplace else self.copy()]
         for i, (src, dest) in enumerate(pairs):
-            new_rb: List["Block"] = []
+            new_rb: List[Block] = []
             for blk in rb:
                 m = masks[i]
                 convert = i == src_len  # only convert once at the end
@@ -998,7 +998,7 @@ def setitem(self, indexer, value):
         block = self.make_block(values)
         return block
 
-    def putmask(self, mask, new, axis: int = 0) -> List["Block"]:
+    def putmask(self, mask, new, axis: int = 0) -> List[Block]:
         """
         putmask the data to the block; it is possible that we may create a
         new dtype of block
@@ -1095,7 +1095,7 @@ def interpolate(
         self,
         method: str = "pad",
         axis: int = 0,
-        index: Optional["Index"] = None,
+        index: Optional[Index] = None,
         inplace: bool = False,
         limit: Optional[int] = None,
         limit_direction: str = "forward",
@@ -1157,7 +1157,7 @@ def _interpolate_with_fill(
         limit: Optional[int] = None,
         limit_area: Optional[str] = None,
         downcast: Optional[str] = None,
-    ) -> List["Block"]:
+    ) -> List[Block]:
         """ fillna but using the interpolate machinery """
         inplace = validate_bool_kwarg(inplace, "inplace")
 
@@ -1188,7 +1188,7 @@ def _interpolate(
         inplace: bool = False,
         downcast: Optional[str] = None,
         **kwargs,
-    ) -> List["Block"]:
+    ) -> List[Block]:
         """ interpolate using scipy wrappers """
         inplace = validate_bool_kwarg(inplace, "inplace")
         data = self.values if inplace else self.values.copy()
@@ -1264,7 +1264,7 @@ def take_nd(self, indexer, axis: int, new_mgr_locs=None, fill_value=lib.no_defau
         else:
             return self.make_block_same_class(new_values, new_mgr_locs)
 
-    def diff(self, n: int, axis: int = 1) -> List["Block"]:
+    def diff(self, n: int, axis: int = 1) -> List[Block]:
         """ return block for the diff of the values """
         new_values = algos.diff(self.values, n, axis=axis, stacklevel=7)
         return [self.make_block(values=new_values)]
@@ -1279,7 +1279,7 @@ def shift(self, periods: int, axis: int = 0, fill_value=None):
 
         return [self.make_block(new_values)]
 
-    def where(self, other, cond, errors="raise", axis: int = 0) -> List["Block"]:
+    def where(self, other, cond, errors="raise", axis: int = 0) -> List[Block]:
         """
         evaluate the block; return result block(s) from the result
 
@@ -1355,7 +1355,7 @@ def where(self, other, cond, errors="raise", axis: int = 0) -> List["Block"]:
         cond = cond.swapaxes(axis, 0)
         mask = np.array([cond[i].all() for i in range(cond.shape[0])], dtype=bool)
 
-        result_blocks: List["Block"] = []
+        result_blocks: List[Block] = []
         for m in [mask, ~mask]:
             if m.any():
                 result = cast(np.ndarray, result)  # EABlock overrides where
@@ -1459,7 +1459,7 @@ def _replace_coerce(
         mask: np.ndarray,
         inplace: bool = True,
         regex: bool = False,
-    ) -> List["Block"]:
+    ) -> List[Block]:
         """
         Replace value corresponding to the given boolean array with another
         value.
@@ -1577,7 +1577,7 @@ def set_inplace(self, locs, values):
         assert locs.tolist() == [0]
         self.values = values
 
-    def putmask(self, mask, new, axis: int = 0) -> List["Block"]:
+    def putmask(self, mask, new, axis: int = 0) -> List[Block]:
         """
         See Block.putmask.__doc__
         """
@@ -1779,7 +1779,7 @@ def interpolate(
             placement=self.mgr_locs,
         )
 
-    def diff(self, n: int, axis: int = 1) -> List["Block"]:
+    def diff(self, n: int, axis: int = 1) -> List[Block]:
         if axis == 0 and n != 0:
             # n==0 case will be a no-op so let is fall through
             # Since we only have one column, the result will be all-NA.
@@ -1794,7 +1794,7 @@ def diff(self, n: int, axis: int = 1) -> List["Block"]:
 
     def shift(
         self, periods: int, axis: int = 0, fill_value: Any = None
-    ) -> List["ExtensionBlock"]:
+    ) -> List[ExtensionBlock]:
         """
         Shift the block by `periods`.
 
@@ -1809,7 +1809,7 @@ def shift(
             )
         ]
 
-    def where(self, other, cond, errors="raise", axis: int = 0) -> List["Block"]:
+    def where(self, other, cond, errors="raise", axis: int = 0) -> List[Block]:
 
         cond = _extract_bool_array(cond)
         assert not isinstance(other, (ABCIndex, ABCSeries, ABCDataFrame))
@@ -1999,7 +1999,7 @@ def iget(self, key):
         # TODO(EA2D): this can be removed if we ever have 2D EA
         return self.array_values().reshape(self.shape)[key]
 
-    def diff(self, n: int, axis: int = 0) -> List["Block"]:
+    def diff(self, n: int, axis: int = 0) -> List[Block]:
         """
         1st discrete difference.
 
@@ -2040,7 +2040,7 @@ def to_native_types(self, na_rep="NaT", **kwargs):
         result = arr._format_native_types(na_rep=na_rep, **kwargs)
         return self.make_block(result)
 
-    def where(self, other, cond, errors="raise", axis: int = 0) -> List["Block"]:
+    def where(self, other, cond, errors="raise", axis: int = 0) -> List[Block]:
         # TODO(EA2D): reshape unnecessary with 2D EAs
         arr = self.array_values().reshape(self.shape)
 
@@ -2329,7 +2329,7 @@ def convert(
         datetime: bool = True,
         numeric: bool = True,
         timedelta: bool = True,
-    ) -> List["Block"]:
+    ) -> List[Block]:
         """
         attempt to cast any object types to better types return a copy of
         the block (if copy = True) by definition we ARE an ObjectBlock!!!!!
@@ -2359,7 +2359,7 @@ def f(mask, val, idx):
 
         return blocks
 
-    def _maybe_downcast(self, blocks: List["Block"], downcast=None) -> List["Block"]:
+    def _maybe_downcast(self, blocks: List[Block], downcast=None) -> List[Block]:
 
         if downcast is not None:
             return blocks
@@ -2376,7 +2376,7 @@ def replace(
         value,
         inplace: bool = False,
         regex: bool = False,
-    ) -> List["Block"]:
+    ) -> List[Block]:
         # Note: the checks we do in NDFrame.replace ensure we never get
         #  here with listlike to_replace or value, as those cases
         #  go through _replace_list
@@ -2412,7 +2412,7 @@ def _replace_list(
         dest_list: List[Any],
         inplace: bool = False,
         regex: bool = False,
-    ) -> List["Block"]:
+    ) -> List[Block]:
         if len(algos.unique(dest_list)) == 1:
             # We likely got here by tiling value inside NDFrame.replace,
             #  so un-tile here
@@ -2425,7 +2425,7 @@ def replace(
         value,
         inplace: bool = False,
         regex: bool = False,
-    ) -> List["Block"]:
+    ) -> List[Block]:
         inplace = validate_bool_kwarg(inplace, "inplace")
         result = self if inplace else self.copy()
 
diff --git a/pandas/core/internals/concat.py b/pandas/core/internals/concat.py
index 32b6f9d64dd8d..0b611bfdb1f10 100644
--- a/pandas/core/internals/concat.py
+++ b/pandas/core/internals/concat.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from collections import defaultdict
 import copy
 import itertools
@@ -36,7 +38,7 @@
 
 
 def concatenate_block_managers(
-    mgrs_indexers, axes: List["Index"], concat_axis: int, copy: bool
+    mgrs_indexers, axes: List[Index], concat_axis: int, copy: bool
 ) -> Manager:
     """
     Concatenate block managers into one.
diff --git a/pandas/core/internals/ops.py b/pandas/core/internals/ops.py
index 562740a275acb..829a60c1889a8 100644
--- a/pandas/core/internals/ops.py
+++ b/pandas/core/internals/ops.py
@@ -51,7 +51,7 @@ def operate_blockwise(
     # At this point we have already checked the parent DataFrames for
     #  assert rframe._indexed_same(lframe)
 
-    res_blks: List["Block"] = []
+    res_blks: List[Block] = []
     for lvals, rvals, locs, left_ea, right_ea, rblk in _iter_block_pairs(left, right):
         res_values = array_op(lvals, rvals)
         if left_ea and not right_ea and hasattr(res_values, "reshape"):
@@ -79,7 +79,7 @@ def operate_blockwise(
     return new_mgr
 
 
-def _reset_block_mgr_locs(nbs: List["Block"], locs):
+def _reset_block_mgr_locs(nbs: List[Block], locs):
     """
     Reset mgr_locs to correspond to our original DataFrame.
     """
diff --git a/pandas/core/reshape/concat.py b/pandas/core/reshape/concat.py
index 7e6ff6ae358bb..f9bff603cec38 100644
--- a/pandas/core/reshape/concat.py
+++ b/pandas/core/reshape/concat.py
@@ -52,7 +52,7 @@
 
 @overload
 def concat(
-    objs: Union[Iterable["DataFrame"], Mapping[Hashable, "DataFrame"]],
+    objs: Union[Iterable[DataFrame], Mapping[Hashable, DataFrame]],
     axis=0,
     join: str = "outer",
     ignore_index: bool = False,
@@ -68,7 +68,7 @@ def concat(
 
 @overload
 def concat(
-    objs: Union[Iterable["NDFrame"], Mapping[Hashable, "NDFrame"]],
+    objs: Union[Iterable[NDFrame], Mapping[Hashable, NDFrame]],
     axis=0,
     join: str = "outer",
     ignore_index: bool = False,
@@ -83,7 +83,7 @@ def concat(
 
 
 def concat(
-    objs: Union[Iterable["NDFrame"], Mapping[Hashable, "NDFrame"]],
+    objs: Union[Iterable[NDFrame], Mapping[Hashable, NDFrame]],
     axis=0,
     join="outer",
     ignore_index: bool = False,
@@ -308,7 +308,7 @@ class _Concatenator:
 
     def __init__(
         self,
-        objs: Union[Iterable["NDFrame"], Mapping[Hashable, "NDFrame"]],
+        objs: Union[Iterable[NDFrame], Mapping[Hashable, NDFrame]],
         axis=0,
         join: str = "outer",
         keys=None,
@@ -377,7 +377,7 @@ def __init__(
         # get the sample
         # want the highest ndim that we have, and must be non-empty
         # unless all objs are empty
-        sample: Optional["NDFrame"] = None
+        sample: Optional[NDFrame] = None
         if len(ndims) > 1:
             max_ndim = max(ndims)
             for obj in objs:
diff --git a/pandas/core/reshape/merge.py b/pandas/core/reshape/merge.py
index 18e3f3a48afdb..a3eef92bacfad 100644
--- a/pandas/core/reshape/merge.py
+++ b/pandas/core/reshape/merge.py
@@ -724,7 +724,7 @@ def _maybe_drop_cross_column(self, result: DataFrame, cross_col: Optional[str]):
 
     def _indicator_pre_merge(
         self, left: DataFrame, right: DataFrame
-    ) -> Tuple["DataFrame", "DataFrame"]:
+    ) -> Tuple[DataFrame, DataFrame]:
 
         columns = left.columns.union(right.columns)
 
@@ -1232,7 +1232,7 @@ def _maybe_coerce_merge_keys(self):
 
     def _create_cross_configuration(
         self, left: DataFrame, right: DataFrame
-    ) -> Tuple["DataFrame", "DataFrame", str, str]:
+    ) -> Tuple[DataFrame, DataFrame, str, str]:
         """
         Creates the configuration to dispatch the cross operation to inner join,
         e.g. adding a join column and resetting parameters. Join column is added
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 3888194305d76..fea9b49fa9e97 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -211,8 +211,8 @@ class Series(base.IndexOpsMixin, generic.NDFrame):
     )
     __hash__ = generic.NDFrame.__hash__
     _mgr: SingleBlockManager
-    div: Callable[["Series", Any], "Series"]
-    rdiv: Callable[["Series", Any], "Series"]
+    div: Callable[[Series, Any], Series]
+    rdiv: Callable[[Series, Any], Series]
 
     # ----------------------------------------------------------------------
     # Constructors
@@ -398,11 +398,11 @@ def _init_dict(self, data, index=None, dtype: Optional[Dtype] = None):
     # ----------------------------------------------------------------------
 
     @property
-    def _constructor(self) -> Type["Series"]:
+    def _constructor(self) -> Type[Series]:
         return Series
 
     @property
-    def _constructor_expanddim(self) -> Type["DataFrame"]:
+    def _constructor_expanddim(self) -> Type[DataFrame]:
         from pandas.core.frame import DataFrame
 
         return DataFrame
@@ -1850,7 +1850,7 @@ def unique(self):
         result = super().unique()
         return result
 
-    def drop_duplicates(self, keep="first", inplace=False) -> Optional["Series"]:
+    def drop_duplicates(self, keep="first", inplace=False) -> Optional[Series]:
         """
         Return Series with duplicate values removed.
 
@@ -2709,7 +2709,7 @@ def _binop(self, other, func, level=None, fill_value=None):
 
     def _construct_result(
         self, result: Union[ArrayLike, Tuple[ArrayLike, ArrayLike]], name: Hashable
-    ) -> Union["Series", Tuple["Series", "Series"]]:
+    ) -> Union[Series, Tuple[Series, Series]]:
         """
         Construct an appropriately-labelled Series from the result of an op.
 
@@ -4401,7 +4401,7 @@ def fillna(
         inplace=False,
         limit=None,
         downcast=None,
-    ) -> Optional["Series"]:
+    ) -> Optional[Series]:
         return super().fillna(
             value=value,
             method=method,
@@ -4859,8 +4859,8 @@ def resample(
         base: Optional[int] = None,
         on=None,
         level=None,
-        origin: Union[str, "TimestampConvertibleTypes"] = "start_day",
-        offset: Optional["TimedeltaConvertibleTypes"] = None,
+        origin: Union[str, TimestampConvertibleTypes] = "start_day",
+        offset: Optional[TimedeltaConvertibleTypes] = None,
     ) -> Resampler:
         return super().resample(
             rule=rule,
diff --git a/pandas/core/sorting.py b/pandas/core/sorting.py
index 8869533be30fb..da0cafb409e56 100644
--- a/pandas/core/sorting.py
+++ b/pandas/core/sorting.py
@@ -517,7 +517,7 @@ def ensure_key_mapped(values, key: Optional[Callable], levels=None):
 def get_flattened_list(
     comp_ids: np.ndarray,
     ngroups: int,
-    levels: Iterable["Index"],
+    levels: Iterable[Index],
     labels: Iterable[np.ndarray],
 ) -> List[Tuple]:
     """Map compressed group id -> key tuple."""
@@ -532,7 +532,7 @@ def get_flattened_list(
 
 
 def get_indexer_dict(
-    label_list: List[np.ndarray], keys: List["Index"]
+    label_list: List[np.ndarray], keys: List[Index]
 ) -> Dict[Union[str, Tuple], np.ndarray]:
     """
     Returns
diff --git a/pandas/core/tools/datetimes.py b/pandas/core/tools/datetimes.py
index 88ce5865ee8c2..47df65650237e 100644
--- a/pandas/core/tools/datetimes.py
+++ b/pandas/core/tools/datetimes.py
@@ -68,13 +68,13 @@
 
     from pandas import Series
 
-# ---------------------------------------------------------------------
-# types used in annotations
+    # ---------------------------------------------------------------------
+    # types used in annotations
 
-ArrayConvertible = Union[List, Tuple, AnyArrayLike, "Series"]
-Scalar = Union[int, float, str]
-DatetimeScalar = TypeVar("DatetimeScalar", Scalar, datetime)
-DatetimeScalarOrArrayConvertible = Union[DatetimeScalar, ArrayConvertible]
+    ArrayConvertible = Union[List, Tuple, AnyArrayLike, Series]
+    Scalar = Union[int, float, str]
+    DatetimeScalar = TypeVar("DatetimeScalar", Scalar, datetime)
+    DatetimeScalarOrArrayConvertible = Union[DatetimeScalar, ArrayConvertible]
 
 
 # ---------------------------------------------------------------------
@@ -571,7 +571,7 @@ def to_datetime(
     infer_datetime_format: bool = ...,
     origin=...,
     cache: bool = ...,
-) -> Union[DatetimeScalar, "NaTType"]:
+) -> Union[DatetimeScalar, NaTType]:
     ...
 
 
@@ -621,7 +621,7 @@ def to_datetime(
     infer_datetime_format: bool = False,
     origin="unix",
     cache: bool = True,
-) -> Union[DatetimeIndex, "Series", DatetimeScalar, "NaTType"]:
+) -> Union[DatetimeIndex, Series, DatetimeScalar, NaTType]:
     """
     Convert argument to datetime.
 
diff --git a/pandas/io/excel/_base.py b/pandas/io/excel/_base.py
index 962ba2c7f9ef7..4fca057976277 100644
--- a/pandas/io/excel/_base.py
+++ b/pandas/io/excel/_base.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import abc
 import datetime
 from distutils.version import LooseVersion
@@ -789,7 +791,7 @@ def save(self):
 
     def __init__(
         self,
-        path: Union[FilePathOrBuffer, "ExcelWriter"],
+        path: Union[FilePathOrBuffer, ExcelWriter],
         engine=None,
         date_format=None,
         datetime_format=None,
diff --git a/pandas/io/excel/_openpyxl.py b/pandas/io/excel/_openpyxl.py
index 7de958df206d5..71e1bf6b43ad5 100644
--- a/pandas/io/excel/_openpyxl.py
+++ b/pandas/io/excel/_openpyxl.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from typing import TYPE_CHECKING, Dict, List, Optional
 
 import numpy as np
@@ -51,7 +53,7 @@ def save(self):
         self.book.save(self.handles.handle)
 
     @classmethod
-    def _convert_to_style_kwargs(cls, style_dict: dict) -> Dict[str, "Serialisable"]:
+    def _convert_to_style_kwargs(cls, style_dict: dict) -> Dict[str, Serialisable]:
         """
         Convert a style_dict to a set of kwargs suitable for initializing
         or updating-on-copy an openpyxl v2 style object.
diff --git a/pandas/io/formats/csvs.py b/pandas/io/formats/csvs.py
index 74756b0c57092..5a7a78b75bb0f 100644
--- a/pandas/io/formats/csvs.py
+++ b/pandas/io/formats/csvs.py
@@ -91,7 +91,7 @@ def na_rep(self) -> str:
         return self.fmt.na_rep
 
     @property
-    def float_format(self) -> Optional["FloatFormatType"]:
+    def float_format(self) -> Optional[FloatFormatType]:
         return self.fmt.float_format
 
     @property
diff --git a/pandas/io/formats/format.py b/pandas/io/formats/format.py
index 2c17551a7c3b9..68d068df601ff 100644
--- a/pandas/io/formats/format.py
+++ b/pandas/io/formats/format.py
@@ -1502,7 +1502,7 @@ def _format_strings(self) -> List[str]:
 class Datetime64Formatter(GenericArrayFormatter):
     def __init__(
         self,
-        values: Union[np.ndarray, "Series", DatetimeIndex, DatetimeArray],
+        values: Union[np.ndarray, Series, DatetimeIndex, DatetimeArray],
         nat_rep: str = "NaT",
         date_format: None = None,
         **kwargs,
diff --git a/pandas/io/formats/style.py b/pandas/io/formats/style.py
index 782562f455607..03e65029fb021 100644
--- a/pandas/io/formats/style.py
+++ b/pandas/io/formats/style.py
@@ -828,7 +828,7 @@ def _compute(self):
 
     def _apply(
         self,
-        func: Callable[..., "Styler"],
+        func: Callable[..., Styler],
         axis: Optional[Axis] = 0,
         subset=None,
         **kwargs,
@@ -867,7 +867,7 @@ def _apply(
 
     def apply(
         self,
-        func: Callable[..., "Styler"],
+        func: Callable[..., Styler],
         axis: Optional[Axis] = 0,
         subset=None,
         **kwargs,
diff --git a/pandas/io/json/_json.py b/pandas/io/json/_json.py
index 0791599dad201..e53c828fe30cb 100644
--- a/pandas/io/json/_json.py
+++ b/pandas/io/json/_json.py
@@ -74,7 +74,7 @@ def to_json(
     if orient == "table" and isinstance(obj, Series):
         obj = obj.to_frame(name=obj.name or "values")
 
-    writer: Type["Writer"]
+    writer: Type[Writer]
     if orient == "table" and isinstance(obj, DataFrame):
         writer = JSONTableWriter
     elif isinstance(obj, Series):
diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py
index 0225811d95244..bd40387e13c90 100644
--- a/pandas/io/pytables.py
+++ b/pandas/io/pytables.py
@@ -529,7 +529,7 @@ class HDFStore:
     >>> store.close()   # only now, data is written to disk
     """
 
-    _handle: Optional["File"]
+    _handle: Optional[File]
     _mode: str
     _complevel: int
     _fletcher32: bool
@@ -1471,7 +1471,7 @@ def walk(self, where="/"):
 
             yield (g._v_pathname.rstrip("/"), groups, leaves)
 
-    def get_node(self, key: str) -> Optional["Node"]:
+    def get_node(self, key: str) -> Optional[Node]:
         """ return the node with the key or None if it does not exist """
         self._check_if_open()
         if not key.startswith("/"):
@@ -1487,7 +1487,7 @@ def get_node(self, key: str) -> Optional["Node"]:
         assert isinstance(node, _table_mod.Node), type(node)
         return node
 
-    def get_storer(self, key: str) -> Union["GenericFixed", "Table"]:
+    def get_storer(self, key: str) -> Union[GenericFixed, Table]:
         """ return the storer object for a key, raise if not in the file """
         group = self.get_node(key)
         if group is None:
@@ -1621,9 +1621,9 @@ def _create_storer(
         value: Optional[FrameOrSeries] = None,
         encoding: str = "UTF-8",
         errors: str = "strict",
-    ) -> Union["GenericFixed", "Table"]:
+    ) -> Union[GenericFixed, Table]:
         """ return a suitable class to operate """
-        cls: Union[Type["GenericFixed"], Type["Table"]]
+        cls: Union[Type[GenericFixed], Type[Table]]
 
         if value is not None and not isinstance(value, (Series, DataFrame)):
             raise TypeError("value must be None, Series, or DataFrame")
@@ -1835,12 +1835,12 @@ class TableIterator:
 
     chunksize: Optional[int]
     store: HDFStore
-    s: Union["GenericFixed", "Table"]
+    s: Union[GenericFixed, Table]
 
     def __init__(
         self,
         store: HDFStore,
-        s: Union["GenericFixed", "Table"],
+        s: Union[GenericFixed, Table],
         func,
         where,
         nrows,
@@ -2363,7 +2363,7 @@ def get_atom_string(cls, shape, itemsize):
         return _tables().StringCol(itemsize=itemsize, shape=shape[0])
 
     @classmethod
-    def get_atom_coltype(cls, kind: str) -> Type["Col"]:
+    def get_atom_coltype(cls, kind: str) -> Type[Col]:
         """ return the PyTables column class for this column """
         if kind.startswith("uint"):
             k4 = kind[4:]
@@ -3989,7 +3989,7 @@ def get_blk_items(mgr):
 
         mgr = frame._mgr
         mgr = cast(BlockManager, mgr)
-        blocks: List["Block"] = list(mgr.blocks)
+        blocks: List[Block] = list(mgr.blocks)
         blk_items: List[Index] = get_blk_items(mgr)
 
         if len(data_columns):
@@ -4010,7 +4010,7 @@ def get_blk_items(mgr):
                 tuple(b_items.tolist()): (b, b_items)
                 for b, b_items in zip(blocks, blk_items)
             }
-            new_blocks: List["Block"] = []
+            new_blocks: List[Block] = []
             new_blk_items = []
             for ea in values_axes:
                 items = tuple(ea.values)
@@ -4906,7 +4906,7 @@ def _maybe_convert_for_string_atom(
     elif not (inferred_type == "string" or dtype_name == "object"):
         return block.values
 
-    blocks: List["Block"] = block.fillna(nan_rep, downcast=False)
+    blocks: List[Block] = block.fillna(nan_rep, downcast=False)
     # Note: because block is always object dtype, fillna goes
     #  through a path such that the result is always a 1-element list
     assert len(blocks) == 1
diff --git a/pandas/io/sas/sasreader.py b/pandas/io/sas/sasreader.py
index d193971a6721e..8888be02dd5ea 100644
--- a/pandas/io/sas/sasreader.py
+++ b/pandas/io/sas/sasreader.py
@@ -1,6 +1,8 @@
 """
 Read SAS sas7bdat or xport files.
 """
+from __future__ import annotations
+
 from abc import ABCMeta, abstractmethod
 from typing import TYPE_CHECKING, Hashable, Optional, Union, overload
 
@@ -53,7 +55,7 @@ def read_sas(
     encoding: Optional[str] = ...,
     chunksize: None = ...,
     iterator: bool = ...,
-) -> Union["DataFrame", ReaderBase]:
+) -> Union[DataFrame, ReaderBase]:
     ...
 
 
@@ -64,7 +66,7 @@ def read_sas(
     encoding: Optional[str] = None,
     chunksize: Optional[int] = None,
     iterator: bool = False,
-) -> Union["DataFrame", ReaderBase]:
+) -> Union[DataFrame, ReaderBase]:
     """
     Read SAS files stored as either XPORT or SAS7BDAT format files.
 
diff --git a/pandas/plotting/_matplotlib/__init__.py b/pandas/plotting/_matplotlib/__init__.py
index 33011e6a66cac..e212127549355 100644
--- a/pandas/plotting/_matplotlib/__init__.py
+++ b/pandas/plotting/_matplotlib/__init__.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from typing import TYPE_CHECKING, Dict, Type
 
 from pandas.plotting._matplotlib.boxplot import (
@@ -31,7 +33,7 @@
 if TYPE_CHECKING:
     from pandas.plotting._matplotlib.core import MPLPlot
 
-PLOT_CLASSES: Dict[str, Type["MPLPlot"]] = {
+PLOT_CLASSES: Dict[str, Type[MPLPlot]] = {
     "line": LinePlot,
     "bar": BarPlot,
     "barh": BarhPlot,
diff --git a/pandas/plotting/_matplotlib/misc.py b/pandas/plotting/_matplotlib/misc.py
index 8e81751d88fa1..068fd571753a2 100644
--- a/pandas/plotting/_matplotlib/misc.py
+++ b/pandas/plotting/_matplotlib/misc.py
@@ -126,7 +126,7 @@ def _get_marker_compat(marker):
 def radviz(
     frame: DataFrame,
     class_column,
-    ax: Optional["Axes"] = None,
+    ax: Optional[Axes] = None,
     color=None,
     colormap=None,
     **kwds,
@@ -214,7 +214,7 @@ def normalize(series):
 def andrews_curves(
     frame: DataFrame,
     class_column,
-    ax: Optional["Axes"] = None,
+    ax: Optional[Axes] = None,
     samples: int = 200,
     color=None,
     colormap=None,
@@ -280,7 +280,7 @@ def f(t):
 
 def bootstrap_plot(
     series: "Series",
-    fig: Optional["Figure"] = None,
+    fig: Optional[Figure] = None,
     size: int = 50,
     samples: int = 500,
     **kwds,
@@ -337,7 +337,7 @@ def parallel_coordinates(
     frame: DataFrame,
     class_column,
     cols=None,
-    ax: Optional["Axes"] = None,
+    ax: Optional[Axes] = None,
     color=None,
     use_columns=False,
     xticks=None,
@@ -413,9 +413,7 @@ def parallel_coordinates(
     return ax
 
 
-def lag_plot(
-    series: "Series", lag: int = 1, ax: Optional["Axes"] = None, **kwds
-) -> Axes:
+def lag_plot(series: Series, lag: int = 1, ax: Optional[Axes] = None, **kwds) -> Axes:
     # workaround because `c='b'` is hardcoded in matplotlib's scatter method
     import matplotlib.pyplot as plt
 
@@ -432,7 +430,7 @@ def lag_plot(
     return ax
 
 
-def autocorrelation_plot(series: "Series", ax: Optional["Axes"] = None, **kwds) -> Axes:
+def autocorrelation_plot(series: Series, ax: Optional[Axes] = None, **kwds) -> Axes:
     import matplotlib.pyplot as plt
 
     n = len(series)
diff --git a/pandas/plotting/_matplotlib/style.py b/pandas/plotting/_matplotlib/style.py
index c88c310d512be..2c9aadd9573cf 100644
--- a/pandas/plotting/_matplotlib/style.py
+++ b/pandas/plotting/_matplotlib/style.py
@@ -31,7 +31,7 @@
 
 def get_standard_colors(
     num_colors: int,
-    colormap: Optional["Colormap"] = None,
+    colormap: Optional[Colormap] = None,
     color_type: str = "default",
     color: Optional[Union[Dict[str, Color], Color, Collection[Color]]] = None,
 ):
@@ -83,7 +83,7 @@ def get_standard_colors(
 def _derive_colors(
     *,
     color: Optional[Union[Color, Collection[Color]]],
-    colormap: Optional[Union[str, "Colormap"]],
+    colormap: Optional[Union[str, Colormap]],
     color_type: str,
     num_colors: int,
 ) -> List[Color]:
@@ -142,7 +142,7 @@ def _cycle_colors(colors: List[Color], num_colors: int) -> Iterator[Color]:
 
 
 def _get_colors_from_colormap(
-    colormap: Union[str, "Colormap"],
+    colormap: Union[str, Colormap],
     num_colors: int,
 ) -> List[Color]:
     """Get colors from colormap."""
@@ -150,7 +150,7 @@ def _get_colors_from_colormap(
     return [colormap(num) for num in np.linspace(0, 1, num=num_colors)]
 
 
-def _get_cmap_instance(colormap: Union[str, "Colormap"]) -> Colormap:
+def _get_cmap_instance(colormap: Union[str, Colormap]) -> Colormap:
     """Get instance of matplotlib colormap."""
     if isinstance(colormap, str):
         cmap = colormap
diff --git a/pandas/plotting/_matplotlib/tools.py b/pandas/plotting/_matplotlib/tools.py
index 7440daeb0a632..978b9721c992c 100644
--- a/pandas/plotting/_matplotlib/tools.py
+++ b/pandas/plotting/_matplotlib/tools.py
@@ -351,7 +351,7 @@ def _has_externally_shared_axis(ax1: "matplotlib.axes", compare_axis: "str") ->
 
 
 def handle_shared_axes(
-    axarr: Iterable["Axes"],
+    axarr: Iterable[Axes],
     nplots: int,
     naxes: int,
     nrows: int,
@@ -404,7 +404,7 @@ def handle_shared_axes(
                     _remove_labels_from_axis(ax.yaxis)
 
 
-def flatten_axes(axes: Union["Axes", Sequence["Axes"]]) -> np.ndarray:
+def flatten_axes(axes: Union[Axes, Sequence[Axes]]) -> np.ndarray:
     if not is_list_like(axes):
         return np.array([axes])
     elif isinstance(axes, (np.ndarray, ABCIndex)):
@@ -413,7 +413,7 @@ def flatten_axes(axes: Union["Axes", Sequence["Axes"]]) -> np.ndarray:
 
 
 def set_ticks_props(
-    axes: Union["Axes", Sequence["Axes"]],
+    axes: Union[Axes, Sequence[Axes]],
     xlabelsize=None,
     xrot=None,
     ylabelsize=None,
@@ -433,7 +433,7 @@ def set_ticks_props(
     return axes
 
 
-def get_all_lines(ax: "Axes") -> List["Line2D"]:
+def get_all_lines(ax: Axes) -> List[Line2D]:
     lines = ax.get_lines()
 
     if hasattr(ax, "right_ax"):
@@ -445,7 +445,7 @@ def get_all_lines(ax: "Axes") -> List["Line2D"]:
     return lines
 
 
-def get_xlim(lines: Iterable["Line2D"]) -> Tuple[float, float]:
+def get_xlim(lines: Iterable[Line2D]) -> Tuple[float, float]:
     left, right = np.inf, -np.inf
     for line in lines:
         x = line.get_xdata(orig=False)
diff --git a/pandas/tests/extension/arrow/arrays.py b/pandas/tests/extension/arrow/arrays.py
index 65c5102e22997..3000e8cdeac1f 100644
--- a/pandas/tests/extension/arrow/arrays.py
+++ b/pandas/tests/extension/arrow/arrays.py
@@ -6,6 +6,8 @@
 multiple dtypes. Not all methods are implemented yet, and the
 current implementation is not efficient.
 """
+from __future__ import annotations
+
 import copy
 import itertools
 import operator
@@ -33,7 +35,7 @@ class ArrowBoolDtype(ExtensionDtype):
     na_value = pa.NULL
 
     @classmethod
-    def construct_array_type(cls) -> Type["ArrowBoolArray"]:
+    def construct_array_type(cls) -> Type[ArrowBoolArray]:
         """
         Return the array type associated with this dtype.
 
@@ -57,7 +59,7 @@ class ArrowStringDtype(ExtensionDtype):
     na_value = pa.NULL
 
     @classmethod
-    def construct_array_type(cls) -> Type["ArrowStringArray"]:
+    def construct_array_type(cls) -> Type[ArrowStringArray]:
         """
         Return the array type associated with this dtype.
 
diff --git a/pandas/tests/extension/arrow/test_timestamp.py b/pandas/tests/extension/arrow/test_timestamp.py
index 29bd3713e9552..bd661ad20bb02 100644
--- a/pandas/tests/extension/arrow/test_timestamp.py
+++ b/pandas/tests/extension/arrow/test_timestamp.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import datetime
 from typing import Type
 
@@ -22,7 +24,7 @@ class ArrowTimestampUSDtype(ExtensionDtype):
     na_value = pa.NULL
 
     @classmethod
-    def construct_array_type(cls) -> Type["ArrowTimestampUSArray"]:
+    def construct_array_type(cls) -> Type[ArrowTimestampUSArray]:
         """
         Return the array type associated with this dtype.
 
diff --git a/pandas/tests/extension/decimal/array.py b/pandas/tests/extension/decimal/array.py
index a713550dafa5c..3abd4fc8c6160 100644
--- a/pandas/tests/extension/decimal/array.py
+++ b/pandas/tests/extension/decimal/array.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import decimal
 import numbers
 import random
@@ -30,7 +32,7 @@ def __repr__(self) -> str:
         return f"DecimalDtype(context={self.context})"
 
     @classmethod
-    def construct_array_type(cls) -> Type["DecimalArray"]:
+    def construct_array_type(cls) -> Type[DecimalArray]:
         """
         Return the array type associated with this dtype.
 
diff --git a/pandas/tests/extension/json/array.py b/pandas/tests/extension/json/array.py
index e3cdeb9c1951f..7d70903dc8c32 100644
--- a/pandas/tests/extension/json/array.py
+++ b/pandas/tests/extension/json/array.py
@@ -11,6 +11,8 @@
 in that case. We *want* the dictionaries to be treated as scalars, so we
 hack around pandas by using UserDicts.
 """
+from __future__ import annotations
+
 from collections import UserDict, abc
 import itertools
 import numbers
@@ -33,7 +35,7 @@ class JSONDtype(ExtensionDtype):
     na_value: Mapping[str, Any] = UserDict()
 
     @classmethod
-    def construct_array_type(cls) -> Type["JSONArray"]:
+    def construct_array_type(cls) -> Type[JSONArray]:
         """
         Return the array type associated with this dtype.
 
diff --git a/pandas/tests/extension/list/array.py b/pandas/tests/extension/list/array.py
index d86f90e58d897..5c5a6bb71240b 100644
--- a/pandas/tests/extension/list/array.py
+++ b/pandas/tests/extension/list/array.py
@@ -3,6 +3,8 @@
 
 The ListArray stores an ndarray of lists.
 """
+from __future__ import annotations
+
 import numbers
 import random
 import string
@@ -22,7 +24,7 @@ class ListDtype(ExtensionDtype):
     na_value = np.nan
 
     @classmethod
-    def construct_array_type(cls) -> Type["ListArray"]:
+    def construct_array_type(cls) -> Type[ListArray]:
         """
         Return the array type associated with this dtype.
 
diff --git a/pandas/tests/plotting/common.py b/pandas/tests/plotting/common.py
index d31f57426a721..29c02916ec6e9 100644
--- a/pandas/tests/plotting/common.py
+++ b/pandas/tests/plotting/common.py
@@ -5,6 +5,8 @@
 ``pytestmark = pytest.mark.slow`` at the module level.
 """
 
+from __future__ import annotations
+
 import os
 from typing import TYPE_CHECKING, Sequence, Union
 import warnings
@@ -184,7 +186,7 @@ def _check_visible(self, collections, visible=True):
             assert patch.get_visible() == visible
 
     def _check_patches_all_filled(
-        self, axes: Union["Axes", Sequence["Axes"]], filled: bool = True
+        self, axes: Union[Axes, Sequence[Axes]], filled: bool = True
     ) -> None:
         """
         Check for each artist whether it is filled or not