TYP: use from __future__ import annotations more - batch 3

Author: simonjayhawkins

pandas/core/internals/base.py

@@ -2,11 +2,9 @@
 Base class for the internal managers. Both BlockManager and ArrayManager
 inherit from this class.
 """
-from typing import (
-    List,
-    Optional,
-    TypeVar,
-)
+from __future__ import annotations
+
+from typing import TypeVar
 
 from pandas._typing import (
     DtypeObj,
@@ -27,7 +25,7 @@ class DataManager(PandasObject):
 
     # TODO share more methods/attributes
 
-    axes: List[Index]
+    axes: list[Index]
 
     @property
     def items(self) -> Index:
@@ -123,7 +121,7 @@ def equals(self, other: object) -> bool:
     def apply(
         self: T,
         f,
-        align_keys: Optional[List[str]] = None,
+        align_keys: list[str] | None = None,
         ignore_failures: bool = False,
         **kwargs,
     ) -> T:
@@ -144,7 +142,7 @@ def array(self):
         return self.arrays[0]  # type: ignore[attr-defined]
 
 
-def interleaved_dtype(dtypes: List[DtypeObj]) -> Optional[DtypeObj]:
+def interleaved_dtype(dtypes: list[DtypeObj]) -> DtypeObj | None:
     """
     Find the common dtype for `blocks`.
 

pandas/core/ops/docstrings.py

@@ -1,10 +1,7 @@
 """
 Templating for ops docstrings
 """
-from typing import (
-    Dict,
-    Optional,
-)
+from __future__ import annotations
 
 
 def make_flex_doc(op_name: str, typ: str) -> str:
@@ -297,7 +294,7 @@ def make_flex_doc(op_name: str, typ: str) -> str:
 
 _returns_tuple = """2-Tuple of Series\n    The result of the operation."""
 
-_op_descriptions: Dict[str, Dict[str, Optional[str]]] = {
+_op_descriptions: dict[str, dict[str, str | None]] = {
     # Arithmetic Operators
     "add": {
         "op": "+",

pandas/core/ops/mask_ops.py

@@ -1,10 +1,7 @@
 """
 Ops for masked arrays.
 """
-from typing import (
-    Optional,
-    Union,
-)
+from __future__ import annotations
 
 import numpy as np
 
@@ -15,10 +12,10 @@
 
 
 def kleene_or(
-    left: Union[bool, np.ndarray],
-    right: Union[bool, np.ndarray],
-    left_mask: Optional[np.ndarray],
-    right_mask: Optional[np.ndarray],
+    left: bool | np.ndarray,
+    right: bool | np.ndarray,
+    left_mask: np.ndarray | None,
+    right_mask: np.ndarray | None,
 ):
     """
     Boolean ``or`` using Kleene logic.
@@ -76,10 +73,10 @@ def kleene_or(
 
 
 def kleene_xor(
-    left: Union[bool, np.ndarray],
-    right: Union[bool, np.ndarray],
-    left_mask: Optional[np.ndarray],
-    right_mask: Optional[np.ndarray],
+    left: bool | np.ndarray,
+    right: bool | np.ndarray,
+    left_mask: np.ndarray | None,
+    right_mask: np.ndarray | None,
 ):
     """
     Boolean ``xor`` using Kleene logic.
@@ -125,10 +122,10 @@ def kleene_xor(
 
 
 def kleene_and(
-    left: Union[bool, libmissing.NAType, np.ndarray],
-    right: Union[bool, libmissing.NAType, np.ndarray],
-    left_mask: Optional[np.ndarray],
-    right_mask: Optional[np.ndarray],
+    left: bool | libmissing.NAType | np.ndarray,
+    right: bool | libmissing.NAType | np.ndarray,
+    left_mask: np.ndarray | None,
+    right_mask: np.ndarray | None,
 ):
     """
     Boolean ``and`` using Kleene logic.

pandas/core/tools/numeric.py

@@ -1,4 +1,4 @@
-from typing import Optional
+from __future__ import annotations
 
 import numpy as np
 
@@ -166,7 +166,7 @@ def to_numeric(arg, errors="raise", downcast=None):
 
     # GH33013: for IntegerArray & FloatingArray extract non-null values for casting
     # save mask to reconstruct the full array after casting
-    mask: Optional[np.ndarray] = None
+    mask: np.ndarray | None = None
     if isinstance(values, NumericArray):
         mask = values._mask
         values = values._data[~mask]

pandas/core/tools/times.py

@@ -1,11 +1,9 @@
+from __future__ import annotations
+
 from datetime import (
     datetime,
     time,
 )
-from typing import (
-    List,
-    Optional,
-)
 
 import numpy as np
 
@@ -61,7 +59,7 @@ def _convert_listlike(arg, format):
         if infer_time_format and format is None:
             format = _guess_time_format_for_array(arg)
 
-        times: List[Optional[time]] = []
+        times: list[time | None] = []
         if format is not None:
             for element in arg:
                 try:

pandas/core/util/numba_.py

@@ -1,11 +1,8 @@
 """Common utilities for Numba operations"""
+from __future__ import annotations
+
 import types
-from typing import (
-    Callable,
-    Dict,
-    Optional,
-    Tuple,
-)
+from typing import Callable
 
 import numpy as np
 
@@ -15,10 +12,10 @@
 from pandas.util.version import Version
 
 GLOBAL_USE_NUMBA: bool = False
-NUMBA_FUNC_CACHE: Dict[Tuple[Callable, str], Callable] = {}
+NUMBA_FUNC_CACHE: dict[tuple[Callable, str], Callable] = {}
 
 
-def maybe_use_numba(engine: Optional[str]) -> bool:
+def maybe_use_numba(engine: str | None) -> bool:
     """Signal whether to use numba routines."""
     return engine == "numba" or (engine is None and GLOBAL_USE_NUMBA)
 
@@ -31,8 +28,8 @@ def set_use_numba(enable: bool = False) -> None:
 
 
 def get_jit_arguments(
-    engine_kwargs: Optional[Dict[str, bool]] = None, kwargs: Optional[Dict] = None
-) -> Tuple[bool, bool, bool]:
+    engine_kwargs: dict[str, bool] | None = None, kwargs: dict | None = None
+) -> tuple[bool, bool, bool]:
     """
     Return arguments to pass to numba.JIT, falling back on pandas default JIT settings.
 

pandas/core/window/expanding.py

@@ -1,10 +1,9 @@
+from __future__ import annotations
+
 from textwrap import dedent
 from typing import (
     Any,
     Callable,
-    Dict,
-    Optional,
-    Tuple,
 )
 
 from pandas._typing import (
@@ -184,10 +183,10 @@ def apply(
         self,
         func: Callable[..., Any],
         raw: bool = False,
-        engine: Optional[str] = None,
-        engine_kwargs: Optional[Dict[str, bool]] = None,
-        args: Optional[Tuple[Any, ...]] = None,
-        kwargs: Optional[Dict[str, Any]] = None,
+        engine: str | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
+        args: tuple[Any, ...] | None = None,
+        kwargs: dict[str, Any] | None = None,
     ):
         return super().apply(
             func,
@@ -217,8 +216,8 @@ def apply(
     def sum(
         self,
         *args,
-        engine: Optional[str] = None,
-        engine_kwargs: Optional[Dict[str, bool]] = None,
+        engine: str | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
         **kwargs,
     ):
         nv.validate_expanding_func("sum", args, kwargs)
@@ -243,8 +242,8 @@ def sum(
     def max(
         self,
         *args,
-        engine: Optional[str] = None,
-        engine_kwargs: Optional[Dict[str, bool]] = None,
+        engine: str | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
         **kwargs,
     ):
         nv.validate_expanding_func("max", args, kwargs)
@@ -269,8 +268,8 @@ def max(
     def min(
         self,
         *args,
-        engine: Optional[str] = None,
-        engine_kwargs: Optional[Dict[str, bool]] = None,
+        engine: str | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
         **kwargs,
     ):
         nv.validate_expanding_func("min", args, kwargs)
@@ -295,8 +294,8 @@ def min(
     def mean(
         self,
         *args,
-        engine: Optional[str] = None,
-        engine_kwargs: Optional[Dict[str, bool]] = None,
+        engine: str | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
         **kwargs,
     ):
         nv.validate_expanding_func("mean", args, kwargs)
@@ -319,8 +318,8 @@ def mean(
     )
     def median(
         self,
-        engine: Optional[str] = None,
-        engine_kwargs: Optional[Dict[str, bool]] = None,
+        engine: str | None = None,
+        engine_kwargs: dict[str, bool] | None = None,
         **kwargs,
     ):
         return super().median(engine=engine, engine_kwargs=engine_kwargs, **kwargs)
@@ -592,8 +591,8 @@ def quantile(
     )
     def cov(
         self,
-        other: Optional[FrameOrSeriesUnion] = None,
-        pairwise: Optional[bool] = None,
+        other: FrameOrSeriesUnion | None = None,
+        pairwise: bool | None = None,
         ddof: int = 1,
         **kwargs,
     ):
@@ -657,8 +656,8 @@ def cov(
     )
     def corr(
         self,
-        other: Optional[FrameOrSeriesUnion] = None,
-        pairwise: Optional[bool] = None,
+        other: FrameOrSeriesUnion | None = None,
+        pairwise: bool | None = None,
         ddof: int = 1,
         **kwargs,
     ):