TYP: use from __future__ import annotations more - batch 4

pandas/compat/numpy/function.py

@@ -15,12 +15,9 @@
 methods that are spread throughout the codebase. This module will make it
 easier to adjust to future upstream changes in the analogous numpy signatures.
 """
-from typing import (
-    Any,
-    Dict,
-    Optional,
-    Union,
-)
+from __future__ import annotations
+
+from typing import Any
 
 from numpy import ndarray
 
@@ -41,7 +38,7 @@ def __init__(
         self,
         defaults,
         fname=None,
-        method: Optional[str] = None,
+        method: str | None = None,
         max_fname_arg_count=None,
     ):
         self.fname = fname
@@ -55,7 +52,7 @@ def __call__(
         kwargs,
         fname=None,
         max_fname_arg_count=None,
-        method: Optional[str] = None,
+        method: str | None = None,
     ) -> None:
         if args or kwargs:
             fname = self.fname if fname is None else fname
@@ -119,7 +116,7 @@ def validate_argmax_with_skipna(skipna, args, kwargs):
     return skipna
 
 
-ARGSORT_DEFAULTS: Dict[str, Optional[Union[int, str]]] = {}
+ARGSORT_DEFAULTS: dict[str, int | str | None] = {}
 ARGSORT_DEFAULTS["axis"] = -1
 ARGSORT_DEFAULTS["kind"] = "quicksort"
 ARGSORT_DEFAULTS["order"] = None
@@ -132,7 +129,7 @@ def validate_argmax_with_skipna(skipna, args, kwargs):
 
 # two different signatures of argsort, this second validation for when the
 # `kind` param is supported
-ARGSORT_DEFAULTS_KIND: Dict[str, Optional[int]] = {}
+ARGSORT_DEFAULTS_KIND: dict[str, int | None] = {}
 ARGSORT_DEFAULTS_KIND["axis"] = -1
 ARGSORT_DEFAULTS_KIND["order"] = None
 validate_argsort_kind = CompatValidator(
@@ -155,7 +152,7 @@ def validate_argsort_with_ascending(ascending, args, kwargs):
     return ascending
 
 
-CLIP_DEFAULTS: Dict[str, Any] = {"out": None}
+CLIP_DEFAULTS: dict[str, Any] = {"out": None}
 validate_clip = CompatValidator(
     CLIP_DEFAULTS, fname="clip", method="both", max_fname_arg_count=3
 )
@@ -176,7 +173,7 @@ def validate_clip_with_axis(axis, args, kwargs):
     return axis
 
 
-CUM_FUNC_DEFAULTS: Dict[str, Any] = {}
+CUM_FUNC_DEFAULTS: dict[str, Any] = {}
 CUM_FUNC_DEFAULTS["dtype"] = None
 CUM_FUNC_DEFAULTS["out"] = None
 validate_cum_func = CompatValidator(
@@ -201,7 +198,7 @@ def validate_cum_func_with_skipna(skipna, args, kwargs, name):
     return skipna
 
 
-ALLANY_DEFAULTS: Dict[str, Optional[bool]] = {}
+ALLANY_DEFAULTS: dict[str, bool | None] = {}
 ALLANY_DEFAULTS["dtype"] = None
 ALLANY_DEFAULTS["out"] = None
 ALLANY_DEFAULTS["keepdims"] = False
@@ -224,28 +221,28 @@ def validate_cum_func_with_skipna(skipna, args, kwargs, name):
     MINMAX_DEFAULTS, fname="max", method="both", max_fname_arg_count=1
 )
 
-RESHAPE_DEFAULTS: Dict[str, str] = {"order": "C"}
+RESHAPE_DEFAULTS: dict[str, str] = {"order": "C"}
 validate_reshape = CompatValidator(
     RESHAPE_DEFAULTS, fname="reshape", method="both", max_fname_arg_count=1
 )
 
-REPEAT_DEFAULTS: Dict[str, Any] = {"axis": None}
+REPEAT_DEFAULTS: dict[str, Any] = {"axis": None}
 validate_repeat = CompatValidator(
     REPEAT_DEFAULTS, fname="repeat", method="both", max_fname_arg_count=1
 )
 
-ROUND_DEFAULTS: Dict[str, Any] = {"out": None}
+ROUND_DEFAULTS: dict[str, Any] = {"out": None}
 validate_round = CompatValidator(
     ROUND_DEFAULTS, fname="round", method="both", max_fname_arg_count=1
 )
 
-SORT_DEFAULTS: Dict[str, Optional[Union[int, str]]] = {}
+SORT_DEFAULTS: dict[str, int | str | None] = {}
 SORT_DEFAULTS["axis"] = -1
 SORT_DEFAULTS["kind"] = "quicksort"
 SORT_DEFAULTS["order"] = None
 validate_sort = CompatValidator(SORT_DEFAULTS, fname="sort", method="kwargs")
 
-STAT_FUNC_DEFAULTS: Dict[str, Optional[Any]] = {}
+STAT_FUNC_DEFAULTS: dict[str, Any | None] = {}
 STAT_FUNC_DEFAULTS["dtype"] = None
 STAT_FUNC_DEFAULTS["out"] = None
 
@@ -279,13 +276,13 @@ def validate_cum_func_with_skipna(skipna, args, kwargs, name):
     MEDIAN_DEFAULTS, fname="median", method="both", max_fname_arg_count=1
 )
 
-STAT_DDOF_FUNC_DEFAULTS: Dict[str, Optional[bool]] = {}
+STAT_DDOF_FUNC_DEFAULTS: dict[str, bool | None] = {}
 STAT_DDOF_FUNC_DEFAULTS["dtype"] = None
 STAT_DDOF_FUNC_DEFAULTS["out"] = None
 STAT_DDOF_FUNC_DEFAULTS["keepdims"] = False
 validate_stat_ddof_func = CompatValidator(STAT_DDOF_FUNC_DEFAULTS, method="kwargs")
 
-TAKE_DEFAULTS: Dict[str, Optional[str]] = {}
+TAKE_DEFAULTS: dict[str, str | None] = {}
 TAKE_DEFAULTS["out"] = None
 TAKE_DEFAULTS["mode"] = "raise"
 validate_take = CompatValidator(TAKE_DEFAULTS, fname="take", method="kwargs")
@@ -392,7 +389,7 @@ def validate_resampler_func(method: str, args, kwargs) -> None:
             raise TypeError("too many arguments passed in")
 
 
-def validate_minmax_axis(axis: Optional[int], ndim: int = 1) -> None:
+def validate_minmax_axis(axis: int | None, ndim: int = 1) -> None:
     """
     Ensure that the axis argument passed to min, max, argmin, or argmax is zero
     or None, as otherwise it will be incorrectly ignored.

pandas/core/array_algos/replace.py

@@ -1,13 +1,13 @@
 """
 Methods used by Block.replace and related methods.
 """
+from __future__ import annotations
+
 import operator
 import re
 from typing import (
     Any,
-    Optional,
     Pattern,
-    Union,
 )
 
 import numpy as np
@@ -42,8 +42,8 @@ def should_use_regex(regex: bool, to_replace: Any) -> bool:
 
 
 def compare_or_regex_search(
-    a: ArrayLike, b: Union[Scalar, Pattern], regex: bool, mask: np.ndarray
-) -> Union[ArrayLike, bool]:
+    a: ArrayLike, b: Scalar | Pattern, regex: bool, mask: np.ndarray
+) -> ArrayLike | bool:
     """
     Compare two array_like inputs of the same shape or two scalar values
 
@@ -65,7 +65,7 @@ def compare_or_regex_search(
         return ~mask
 
     def _check_comparison_types(
-        result: Union[ArrayLike, bool], a: ArrayLike, b: Union[Scalar, Pattern]
+        result: ArrayLike | bool, a: ArrayLike, b: Scalar | Pattern
     ):
         """
         Raises an error if the two arrays (a,b) cannot be compared.
@@ -115,7 +115,7 @@ def _check_comparison_types(
     return result
 
 
-def replace_regex(values: ArrayLike, rx: re.Pattern, value, mask: Optional[np.ndarray]):
+def replace_regex(values: ArrayLike, rx: re.Pattern, value, mask: np.ndarray | None):
     """
     Parameters
     ----------

pandas/core/computation/expr.py

@@ -1,6 +1,7 @@
 """
 :func:`~pandas.eval` parsers.
 """
+from __future__ import annotations
 
 import ast
 from functools import (
@@ -11,10 +12,6 @@
 import tokenize
 from typing import (
     Callable,
-    Optional,
-    Set,
-    Tuple,
-    Type,
     TypeVar,
 )
 
@@ -50,7 +47,7 @@
 import pandas.io.formats.printing as printing
 
 
-def _rewrite_assign(tok: Tuple[int, str]) -> Tuple[int, str]:
+def _rewrite_assign(tok: tuple[int, str]) -> tuple[int, str]:
     """
     Rewrite the assignment operator for PyTables expressions that use ``=``
     as a substitute for ``==``.
@@ -69,7 +66,7 @@ def _rewrite_assign(tok: Tuple[int, str]) -> Tuple[int, str]:
     return toknum, "==" if tokval == "=" else tokval
 
 
-def _replace_booleans(tok: Tuple[int, str]) -> Tuple[int, str]:
+def _replace_booleans(tok: tuple[int, str]) -> tuple[int, str]:
     """
     Replace ``&`` with ``and`` and ``|`` with ``or`` so that bitwise
     precedence is changed to boolean precedence.
@@ -94,7 +91,7 @@ def _replace_booleans(tok: Tuple[int, str]) -> Tuple[int, str]:
     return toknum, tokval
 
 
-def _replace_locals(tok: Tuple[int, str]) -> Tuple[int, str]:
+def _replace_locals(tok: tuple[int, str]) -> tuple[int, str]:
     """
     Replace local variables with a syntactically valid name.
 
@@ -271,7 +268,7 @@ def f(self, *args, **kwargs):
 _T = TypeVar("_T", bound="BaseExprVisitor")
 
 
-def disallow(nodes: Set[str]) -> Callable[[Type[_T]], Type[_T]]:
+def disallow(nodes: set[str]) -> Callable[[type[_T]], type[_T]]:
     """
     Decorator to disallow certain nodes from parsing. Raises a
     NotImplementedError instead.
@@ -281,7 +278,7 @@ def disallow(nodes: Set[str]) -> Callable[[Type[_T]], Type[_T]]:
     callable
     """
 
-    def disallowed(cls: Type[_T]) -> Type[_T]:
+    def disallowed(cls: type[_T]) -> type[_T]:
         cls.unsupported_nodes = ()
         for node in nodes:
             new_method = _node_not_implemented(node)
@@ -352,7 +349,7 @@ class BaseExprVisitor(ast.NodeVisitor):
     preparser : callable
     """
 
-    const_type: Type[Term] = Constant
+    const_type: type[Term] = Constant
     term_type = Term
 
     binary_ops = CMP_OPS_SYMS + BOOL_OPS_SYMS + ARITH_OPS_SYMS
@@ -390,7 +387,7 @@ class BaseExprVisitor(ast.NodeVisitor):
         ast.NotIn: ast.NotIn,
     }
 
-    unsupported_nodes: Tuple[str, ...]
+    unsupported_nodes: tuple[str, ...]
 
     def __init__(self, env, engine, parser, preparser=_preparse):
         self.env = env
@@ -798,7 +795,7 @@ def __init__(
         expr,
         engine: str = "numexpr",
         parser: str = "pandas",
-        env: Optional[Scope] = None,
+        env: Scope | None = None,
         level: int = 0,
     ):
         self.expr = expr

pandas/core/computation/expressions.py

@@ -5,12 +5,9 @@
 Offer fast expression evaluation through numexpr
 
 """
+from __future__ import annotations
+
 import operator
-from typing import (
-    List,
-    Optional,
-    Set,
-)
 import warnings
 
 import numpy as np
@@ -25,11 +22,11 @@
 if NUMEXPR_INSTALLED:
     import numexpr as ne
 
-_TEST_MODE: Optional[bool] = None
-_TEST_RESULT: List[bool] = []
+_TEST_MODE: bool | None = None
+_TEST_RESULT: list[bool] = []
 USE_NUMEXPR = NUMEXPR_INSTALLED
-_evaluate: Optional[FuncType] = None
-_where: Optional[FuncType] = None
+_evaluate: FuncType | None = None
+_where: FuncType | None = None
 
 # the set of dtypes that we will allow pass to numexpr
 _ALLOWED_DTYPES = {
@@ -79,7 +76,7 @@ def _can_use_numexpr(op, op_str, a, b, dtype_check):
         # required min elements (otherwise we are adding overhead)
         if a.size > _MIN_ELEMENTS:
             # check for dtype compatibility
-            dtypes: Set[str] = set()
+            dtypes: set[str] = set()
             for o in [a, b]:
                 # ndarray and Series Case
                 if hasattr(o, "dtype"):
@@ -277,7 +274,7 @@ def _store_test_result(used_numexpr: bool) -> None:
         _TEST_RESULT.append(used_numexpr)
 
 
-def get_test_result() -> List[bool]:
+def get_test_result() -> list[bool]:
     """
     Get test result and reset test_results.
     """

pandas/core/dtypes/common.py

@@ -1,11 +1,11 @@
 """
 Common type operations.
 """
+from __future__ import annotations
 
 from typing import (
     Any,
     Callable,
-    Union,
 )
 import warnings
 
@@ -102,7 +102,7 @@ def ensure_float(arr):
 ensure_object = algos.ensure_object
 
 
-def ensure_str(value: Union[bytes, Any]) -> str:
+def ensure_str(value: bytes | Any) -> str:
     """
     Ensure that bytes and non-strings get converted into ``str`` objects.
     """
@@ -113,7 +113,7 @@ def ensure_str(value: Union[bytes, Any]) -> str:
     return value
 
 
-def ensure_python_int(value: Union[int, np.integer]) -> int:
+def ensure_python_int(value: int | np.integer) -> int:
     """
     Ensure that a value is a python int.
 

pandas/core/indexes/timedeltas.py

@@ -1,4 +1,5 @@
 """ implement the TimedeltaIndex """
+from __future__ import annotations
 
 from pandas._libs import (
     index as libindex,

pandas/io/parsers/python_parser.py

@@ -18,10 +18,7 @@
 import numpy as np
 
 import pandas._libs.lib as lib
-from pandas._typing import (
-    FilePathOrBuffer,
-    Union,
-)
+from pandas._typing import FilePathOrBuffer
 from pandas.errors import (
     EmptyDataError,
     ParserError,
@@ -42,7 +39,7 @@
 
 
 class PythonParser(ParserBase):
-    def __init__(self, f: Union[FilePathOrBuffer, list], **kwds):
+    def __init__(self, f: FilePathOrBuffer | list, **kwds):
         """
         Workhorse function for processing nested list into DataFrame
         """