STY: remove --keep-runtime-typing from pyupgrade #40759

Author: fathomer

.pre-commit-config.yaml

@@ -54,7 +54,7 @@ repos:
     rev: v2.11.0
     hooks:
     -   id: pyupgrade
-        args: [--py37-plus, --keep-runtime-typing]
+        args: [--py37-plus]
 -   repo: https://github.com/pre-commit/pygrep-hooks
     rev: v1.8.0
     hooks:

pandas/_testing/__init__.py

@@ -14,8 +14,6 @@
     ContextManager,
     Counter,
     Iterable,
-    List,
-    Type,
 )
 import warnings
 
@@ -116,24 +114,24 @@
 _N = 30
 _K = 4
 
-UNSIGNED_INT_DTYPES: List[Dtype] = ["uint8", "uint16", "uint32", "uint64"]
-UNSIGNED_EA_INT_DTYPES: List[Dtype] = ["UInt8", "UInt16", "UInt32", "UInt64"]
-SIGNED_INT_DTYPES: List[Dtype] = [int, "int8", "int16", "int32", "int64"]
-SIGNED_EA_INT_DTYPES: List[Dtype] = ["Int8", "Int16", "Int32", "Int64"]
+UNSIGNED_INT_DTYPES: list[Dtype] = ["uint8", "uint16", "uint32", "uint64"]
+UNSIGNED_EA_INT_DTYPES: list[Dtype] = ["UInt8", "UInt16", "UInt32", "UInt64"]
+SIGNED_INT_DTYPES: list[Dtype] = [int, "int8", "int16", "int32", "int64"]
+SIGNED_EA_INT_DTYPES: list[Dtype] = ["Int8", "Int16", "Int32", "Int64"]
 ALL_INT_DTYPES = UNSIGNED_INT_DTYPES + SIGNED_INT_DTYPES
 ALL_EA_INT_DTYPES = UNSIGNED_EA_INT_DTYPES + SIGNED_EA_INT_DTYPES
 
-FLOAT_DTYPES: List[Dtype] = [float, "float32", "float64"]
-FLOAT_EA_DTYPES: List[Dtype] = ["Float32", "Float64"]
-COMPLEX_DTYPES: List[Dtype] = [complex, "complex64", "complex128"]
-STRING_DTYPES: List[Dtype] = [str, "str", "U"]
+FLOAT_DTYPES: list[Dtype] = [float, "float32", "float64"]
+FLOAT_EA_DTYPES: list[Dtype] = ["Float32", "Float64"]
+COMPLEX_DTYPES: list[Dtype] = [complex, "complex64", "complex128"]
+STRING_DTYPES: list[Dtype] = [str, "str", "U"]
 
-DATETIME64_DTYPES: List[Dtype] = ["datetime64[ns]", "M8[ns]"]
-TIMEDELTA64_DTYPES: List[Dtype] = ["timedelta64[ns]", "m8[ns]"]
+DATETIME64_DTYPES: list[Dtype] = ["datetime64[ns]", "M8[ns]"]
+TIMEDELTA64_DTYPES: list[Dtype] = ["timedelta64[ns]", "m8[ns]"]
 
-BOOL_DTYPES: List[Dtype] = [bool, "bool"]
-BYTES_DTYPES: List[Dtype] = [bytes, "bytes"]
-OBJECT_DTYPES: List[Dtype] = [object, "object"]
+BOOL_DTYPES: list[Dtype] = [bool, "bool"]
+BYTES_DTYPES: list[Dtype] = [bytes, "bytes"]
+OBJECT_DTYPES: list[Dtype] = [object, "object"]
 
 ALL_REAL_DTYPES = FLOAT_DTYPES + ALL_INT_DTYPES
 ALL_NUMPY_DTYPES = (
@@ -428,7 +426,7 @@ def all_timeseries_index_generator(k: int = 10) -> Iterable[Index]:
     ----------
     k: length of each of the index instances
     """
-    make_index_funcs: List[Callable[..., Index]] = [
+    make_index_funcs: list[Callable[..., Index]] = [
         makeDateIndex,
         makePeriodIndex,
         makeTimedeltaIndex,
@@ -876,7 +874,7 @@ def skipna_wrapper(x):
     return skipna_wrapper
 
 
-def convert_rows_list_to_csv_str(rows_list: List[str]):
+def convert_rows_list_to_csv_str(rows_list: list[str]):
     """
     Convert list of CSV rows to single CSV-formatted string for current OS.
 
@@ -896,7 +894,7 @@ def convert_rows_list_to_csv_str(rows_list: List[str]):
     return sep.join(rows_list) + sep
 
 
-def external_error_raised(expected_exception: Type[Exception]) -> ContextManager:
+def external_error_raised(expected_exception: type[Exception]) -> ContextManager:
     """
     Helper function to mark pytest.raises that have an external error message.
 

pandas/_typing.py

@@ -25,7 +25,7 @@
     Optional,
     Sequence,
     Tuple,
-    Type,
+    Type as Type_T,
     TypeVar,
     Union,
 )
@@ -119,7 +119,7 @@
 # dtypes
 NpDtype = Union[str, np.dtype]
 Dtype = Union[
-    "ExtensionDtype", NpDtype, Type[Union[str, float, int, complex, bool, object]]
+    "ExtensionDtype", NpDtype, Type_T[Union[str, float, int, complex, bool, object]]
 ]
 # DtypeArg specifies all allowable dtypes in a functions its dtype argument
 DtypeArg = Union[Dtype, Dict[Hashable, Dtype]]

pandas/compat/pickle_compat.py

@@ -7,10 +7,7 @@
 import copy
 import io
 import pickle as pkl
-from typing import (
-    TYPE_CHECKING,
-    Optional,
-)
+from typing import TYPE_CHECKING
 import warnings
 
 from pandas._libs.tslibs import BaseOffset
@@ -235,7 +232,7 @@ def load_newobj_ex(self):
     pass
 
 
-def load(fh, encoding: Optional[str] = None, is_verbose: bool = False):
+def load(fh, encoding: str | None = None, is_verbose: bool = False):
     """
     Load a pickle, with a provided encoding,
 

pandas/core/aggregation.py

@@ -12,14 +12,9 @@
     Any,
     Callable,
     DefaultDict,
-    Dict,
     Hashable,
     Iterable,
-    List,
-    Optional,
     Sequence,
-    Tuple,
-    Union,
 )
 
 from pandas._typing import (
@@ -42,8 +37,8 @@
 
 
 def reconstruct_func(
-    func: Optional[AggFuncType], **kwargs
-) -> Tuple[bool, Optional[AggFuncType], Optional[List[str]], Optional[List[int]]]:
+    func: AggFuncType | None, **kwargs
+) -> tuple[bool, AggFuncType | None, list[str] | None, list[int] | None]:
     """
     This is the internal function to reconstruct func given if there is relabeling
     or not and also normalize the keyword to get new order of columns.
@@ -81,8 +76,8 @@ def reconstruct_func(
     (False, 'min', None, None)
     """
     relabeling = func is None and is_multi_agg_with_relabel(**kwargs)
-    columns: Optional[List[str]] = None
-    order: Optional[List[int]] = None
+    columns: list[str] | None = None
+    order: list[int] | None = None
 
     if not relabeling:
         if isinstance(func, list) and len(func) > len(set(func)):
@@ -129,7 +124,7 @@ def is_multi_agg_with_relabel(**kwargs) -> bool:
     )
 
 
-def normalize_keyword_aggregation(kwargs: dict) -> Tuple[dict, List[str], List[int]]:
+def normalize_keyword_aggregation(kwargs: dict) -> tuple[dict, list[str], list[int]]:
     """
     Normalize user-provided "named aggregation" kwargs.
     Transforms from the new ``Mapping[str, NamedAgg]`` style kwargs
@@ -187,8 +182,8 @@ def normalize_keyword_aggregation(kwargs: dict) -> Tuple[dict, List[str], List[i
 
 
 def _make_unique_kwarg_list(
-    seq: Sequence[Tuple[Any, Any]]
-) -> Sequence[Tuple[Any, Any]]:
+    seq: Sequence[tuple[Any, Any]]
+) -> Sequence[tuple[Any, Any]]:
     """
     Uniquify aggfunc name of the pairs in the order list
 
@@ -292,10 +287,10 @@ def maybe_mangle_lambdas(agg_spec: Any) -> Any:
 
 def relabel_result(
     result: FrameOrSeries,
-    func: Dict[str, List[Union[Callable, str]]],
+    func: dict[str, list[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.
@@ -322,7 +317,7 @@ 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
@@ -366,7 +361,7 @@ def relabel_result(
 
 def validate_func_kwargs(
     kwargs: dict,
-) -> Tuple[List[str], List[Union[str, Callable[..., Any]]]]:
+) -> tuple[list[str], list[str | Callable[..., Any]]]:
     """
     Validates types of user-provided "named aggregation" kwargs.
     `TypeError` is raised if aggfunc is not `str` or callable.

pandas/core/algorithms.py

@@ -8,9 +8,6 @@
 from textwrap import dedent
 from typing import (
     TYPE_CHECKING,
-    Dict,
-    Optional,
-    Tuple,
     Union,
     cast,
 )
@@ -103,13 +100,13 @@
         TimedeltaArray,
     )
 
-_shared_docs: Dict[str, str] = {}
+_shared_docs: dict[str, str] = {}
 
 
 # --------------- #
 # dtype access    #
 # --------------- #
-def _ensure_data(values: ArrayLike) -> Tuple[np.ndarray, DtypeObj]:
+def _ensure_data(values: ArrayLike) -> tuple[np.ndarray, DtypeObj]:
     """
     routine to ensure that our data is of the correct
     input dtype for lower-level routines
@@ -542,10 +539,10 @@ def f(c, v):
 def factorize_array(
     values: np.ndarray,
     na_sentinel: int = -1,
-    size_hint: Optional[int] = None,
+    size_hint: int | None = None,
     na_value=None,
-    mask: Optional[np.ndarray] = None,
-) -> Tuple[np.ndarray, np.ndarray]:
+    mask: np.ndarray | None = None,
+) -> tuple[np.ndarray, np.ndarray]:
     """
     Factorize an array-like to codes and uniques.
 
@@ -608,9 +605,9 @@ def factorize_array(
 def factorize(
     values,
     sort: bool = False,
-    na_sentinel: Optional[int] = -1,
-    size_hint: Optional[int] = None,
-) -> Tuple[np.ndarray, Union[np.ndarray, Index]]:
+    na_sentinel: int | None = -1,
+    size_hint: int | None = None,
+) -> tuple[np.ndarray, np.ndarray | Index]:
     """
     Encode the object as an enumerated type or categorical variable.
 
@@ -926,7 +923,7 @@ def value_counts_arraylike(values, dropna: bool):
     return keys, counts
 
 
-def duplicated(values: ArrayLike, keep: Union[str, bool] = "first") -> np.ndarray:
+def duplicated(values: ArrayLike, keep: str | bool = "first") -> np.ndarray:
     """
     Return boolean ndarray denoting duplicate values.
 
@@ -1062,8 +1059,8 @@ def rank(
 def checked_add_with_arr(
     arr: np.ndarray,
     b,
-    arr_mask: Optional[np.ndarray] = None,
-    b_mask: Optional[np.ndarray] = None,
+    arr_mask: np.ndarray | None = None,
+    b_mask: np.ndarray | None = None,
 ) -> np.ndarray:
     """
     Perform array addition that checks for underflow and overflow.
@@ -1741,7 +1738,7 @@ def safe_sort(
     na_sentinel: int = -1,
     assume_unique: bool = False,
     verify: bool = True,
-) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
+) -> np.ndarray | tuple[np.ndarray, np.ndarray]:
     """
     Sort ``values`` and reorder corresponding ``codes``.