remove np.ndarray

Author: cmp0xff

pandas-stubs/_typing.pyi

@@ -784,7 +784,7 @@ TimeNonexistent: TypeAlias = (
 DropKeep: TypeAlias = Literal["first", "last", False]
 CorrelationMethod: TypeAlias = (
     Literal["pearson", "kendall", "spearman"]
-    | Callable[[np.ndarray, np.ndarray], float]
+    | Callable[[np.typing.NDArray[Any], np.typing.NDArray[Any]], float]
 )
 AlignJoin: TypeAlias = Literal["outer", "inner", "left", "right"]
 DtypeBackend: TypeAlias = Literal["pyarrow", "numpy_nullable"]
@@ -858,6 +858,7 @@ ShapeT = TypeVar("ShapeT", bound=tuple[int, ...], default=tuple[Any, ...])
 np_ndarray: TypeAlias = np.ndarray[ShapeT, np.dtype[GenericT]]
 # Numpy arrays with known shape (Do not use as argument types, only as return types)
 np_1darray: TypeAlias = np.ndarray[tuple[int], np.dtype[GenericT]]
+np_1darray_bool: TypeAlias = np_1darray[np.bool]
 np_1darray_int64: TypeAlias = np_1darray[np.int64]
 np_1darray_float: TypeAlias = np_1darray[np.floating]
 

pandas-stubs/core/indexers.pyi

@@ -1,16 +1,14 @@
 from typing import Any
 
-import numpy as np
 from numpy import typing as npt
 
 from pandas._typing import (
     AnyArrayLike,
-    np_1darray,
 )
 
 def check_array_indexer(
     arrayArrayLike: AnyArrayLike, indexer: AnyArrayLike
-) -> np_1darray[np.bool_]: ...
+) -> np_1darray_bool: ...
 
 class BaseIndexer:
     def __init__(

tests/__init__.py

@@ -45,25 +45,31 @@
         UIntDtypeArg as UIntDtypeArg,
         VoidDtypeArg as VoidDtypeArg,
         np_1darray as np_1darray,
+        np_1darray_bool as np_1darray_bool,
         np_1darray_float as np_1darray_float,
         np_1darray_int64 as np_1darray_int64,
         np_2darray as np_2darray,
         np_ndarray as np_ndarray,
         np_ndarray_bool as np_ndarray_bool,
+        np_ndarray_dt as np_ndarray_dt,
         np_ndarray_int as np_ndarray_int,
+        np_ndarray_td as np_ndarray_td,
     )
 else:
     _G = TypeVar("_G", bound=np.generic)
     _S = TypeVar("_S", bound=tuple[int, ...])
     # Separately define here so pytest works
     np_1darray: TypeAlias = np.ndarray[tuple[int], np.dtype[_G]]
+    np_1darray_bool: TypeAlias = np_1darray[np.bool]
     np_1darray_int64: TypeAlias = np_1darray[np.int64]
     np_1darray_float: TypeAlias = np_1darray[np.floating]
     np_2darray: TypeAlias = np.ndarray[tuple[int, int], np.dtype[_G]]
     np_ndarray: TypeAlias = np.ndarray[_S, np.dtype[_G]]
     np_ndarray_bool: TypeAlias = npt.NDArray[np.bool_]
+    np_ndarray_dt: TypeAlias = npt.NDArray[np.datetime64]
     np_ndarray_int: TypeAlias = npt.NDArray[np.signedinteger]
     np_ndarray_int64: TypeAlias = npt.NDArray[np.int64]
+    np_ndarray_td: TypeAlias = npt.NDArray[np.timedelta64]
 
 TYPE_CHECKING_INVALID_USAGE: Final = TYPE_CHECKING
 WINDOWS = os.name == "nt" or "cygwin" in platform.system().lower()

tests/extension/decimal/array.py

@@ -11,7 +11,6 @@
 import numbers
 import sys
 from typing import (
-    TYPE_CHECKING,
     Any,
     cast,
     overload,
@@ -53,8 +52,11 @@
     pandas_dtype,
 )
 
-if TYPE_CHECKING:
-    from pandas._typing import np_1darray
+from tests import (
+    np_1darray,
+    np_1darray_bool,
+    np_ndarray,
+)
 
 
 @register_extension_dtype
@@ -94,7 +96,7 @@ class DecimalArray(OpsMixin, ExtensionScalarOpsMixin, ExtensionArray):
 
     def __init__(
         self,
-        values: MutableSequence[decimal._DecimalNew] | np.ndarray | ExtensionArray,
+        values: MutableSequence[decimal._DecimalNew] | np_ndarray | ExtensionArray,
         dtype: DecimalDtype | None = None,
         copy: bool = False,
         context: decimal.Context | None = None,
@@ -126,7 +128,7 @@ def dtype(self) -> DecimalDtype:
     @classmethod
     def _from_sequence(
         cls,
-        scalars: list[decimal._DecimalNew] | np.ndarray | ExtensionArray,
+        scalars: list[decimal._DecimalNew] | np_ndarray | ExtensionArray,
         dtype: DecimalDtype | None = None,
         copy: bool = False,
     ) -> Self:
@@ -144,7 +146,7 @@ def _from_sequence_of_strings(
     @classmethod
     def _from_factorized(
         cls,
-        values: list[decimal._DecimalNew] | np.ndarray | ExtensionArray,
+        values: list[decimal._DecimalNew] | np_ndarray | ExtensionArray,
         original: Any,
     ) -> Self:
         return cls(values)
@@ -157,7 +159,7 @@ def to_numpy(
         copy: bool = False,
         na_value: object = no_default,
         decimals: int | None = None,
-    ) -> np.ndarray:
+    ) -> np_ndarray:
         result = np.asarray(self, dtype=dtype)
         if decimals is not None:
             result = np.asarray([round(x, decimals) for x in result])
@@ -192,7 +194,7 @@ def reconstruct(
                 decimal.Decimal
                 | numbers.Number
                 | list[decimal._DecimalNew]
-                | np.ndarray
+                | np_ndarray
             ),
         ) -> decimal.Decimal | numbers.Number | DecimalArray:
             if isinstance(x, (decimal.Decimal, numbers.Number)):
@@ -281,7 +283,7 @@ def nbytes(self) -> int:
             return n * sys.getsizeof(self[0])
         return 0
 
-    def isna(self) -> np_1darray[np.bool_]:
+    def isna(self) -> np_1darray_bool:
         if sys.version_info < (3, 11):
             return np.array([x.is_nan() for x in self._data], bool)  # type: ignore[return-value] # pyright: ignore[reportReturnType]
         return np.array([x.is_nan() for x in self._data], bool)
@@ -320,7 +322,7 @@ def _reduce(self, name: str, *, skipna: bool = True, **kwargs: Any) -> Any:
 
     def _cmp_method(
         self, other: Any, op: Callable[[Self, ExtensionArray | list[Any]], bool]
-    ) -> np_1darray[np.bool_]:
+    ) -> np_1darray_bool:
         # For use with OpsMixin
         def convert_values(param: Any) -> ExtensionArray | list[Any]:
             if isinstance(param, ExtensionArray) or is_list_like(param):
@@ -337,9 +339,7 @@ def convert_values(param: Any) -> ExtensionArray | list[Any]:
         # a TypeError should be raised
         res = [op(a, b) for (a, b) in zip(lvalues, rvalues)]
 
-        return cast(
-            np.ndarray[tuple[int], np.dtype[np.bool_]], np.asarray(res, dtype=bool)
-        )
+        return cast(np_1darray_bool, np.asarray(res, dtype=bool))
 
     def value_counts(self, dropna: bool = True) -> Series:
         from pandas.core.algorithms import value_counts