Merge branch 'master' into enh-masked-2d

pandas/core/arrays/base.py

@@ -1194,7 +1194,7 @@ def _repr_2d(self) -> str:
         class_name = f"<{type(self).__name__}>"
         return f"{class_name}\n[\n{data}\n]\nShape: {self.shape}, dtype: {self.dtype}"
 
-    def _formatter(self, boxed: bool = False) -> Callable[[Any], Optional[str]]:
+    def _formatter(self, boxed: bool = False) -> Callable[[Any], str | None]:
         """
         Formatting function for scalar values.
 

pandas/core/arrays/boolean.py

@@ -437,7 +437,7 @@ def _values_for_argsort(self) -> np.ndarray:
         data[self._mask] = -1
         return data
 
-    def any(self, *, skipna: bool = True, axis: Optional[int] = 0, **kwargs):
+    def any(self, *, skipna: bool = True, axis: int | None = 0, **kwargs):
         """
         Return whether any element is True.
 
@@ -507,7 +507,7 @@ def any(self, *, skipna: bool = True, axis: Optional[int] = 0, **kwargs):
             else:
                 return self.dtype.na_value
 
-    def all(self, *, skipna: bool = True, axis: Optional[int] = 0, **kwargs):
+    def all(self, *, skipna: bool = True, axis: int | None = 0, **kwargs):
         """
         Return whether all elements are True.
 

pandas/core/arrays/categorical.py

@@ -1367,7 +1367,7 @@ def _validate_fill_value(self, fill_value):
     # -------------------------------------------------------------
 
     @ravel_compat
-    def __array__(self, dtype: Optional[NpDtype] = None) -> np.ndarray:
+    def __array__(self, dtype: NpDtype | None = None) -> np.ndarray:
         """
         The numpy array interface.
 

pandas/core/arrays/floating.py

@@ -366,19 +366,19 @@ def _cmp_method(self, other, op):
 
         return BooleanArray(result, mask)
 
-    def sum(self, *, skipna=True, min_count=0, axis: Optional[int] = 0, **kwargs):
+    def sum(self, *, skipna=True, min_count=0, axis: int | None = 0, **kwargs):
         nv.validate_sum((), kwargs)
         return super()._reduce("sum", skipna=skipna, min_count=min_count, axis=axis)
 
-    def prod(self, *, skipna=True, min_count=0, axis: Optional[int] = 0, **kwargs):
+    def prod(self, *, skipna=True, min_count=0, axis: int | None = 0, **kwargs):
         nv.validate_prod((), kwargs)
         return super()._reduce("prod", skipna=skipna, min_count=min_count, axis=axis)
 
-    def min(self, *, skipna=True, axis: Optional[int] = 0, **kwargs):
+    def min(self, *, skipna=True, axis: int | None = 0, **kwargs):
         nv.validate_min((), kwargs)
         return super()._reduce("min", skipna=skipna, axis=axis)
 
-    def max(self, *, skipna=True, axis: Optional[int] = 0, **kwargs):
+    def max(self, *, skipna=True, axis: int | None = 0, **kwargs):
         nv.validate_max((), kwargs)
         return super()._reduce("max", skipna=skipna, axis=axis)
 

pandas/core/arrays/integer.py

@@ -441,19 +441,19 @@ def _cmp_method(self, other, op):
 
         return BooleanArray(result, mask)
 
-    def sum(self, *, skipna=True, min_count=0, axis: Optional[int] = 0, **kwargs):
+    def sum(self, *, skipna=True, min_count=0, axis: int | None = 0, **kwargs):
         nv.validate_sum((), kwargs)
         return super()._reduce("sum", skipna=skipna, min_count=min_count, axis=axis)
 
-    def prod(self, *, skipna=True, min_count=0, axis: Optional[int] = 0, **kwargs):
+    def prod(self, *, skipna=True, min_count=0, axis: int | None = 0, **kwargs):
         nv.validate_prod((), kwargs)
         return super()._reduce("prod", skipna=skipna, min_count=min_count, axis=axis)
 
-    def min(self, *, skipna=True, axis: Optional[int] = 0, **kwargs):
+    def min(self, *, skipna=True, axis: int | None = 0, **kwargs):
         nv.validate_min((), kwargs)
         return super()._reduce("min", skipna=skipna, axis=axis)
 
-    def max(self, *, skipna=True, axis: Optional[int] = 0, **kwargs):
+    def max(self, *, skipna=True, axis: int | None = 0, **kwargs):
         nv.validate_max((), kwargs)
         return super()._reduce("max", skipna=skipna, axis=axis)
 

pandas/core/arrays/masked.py

@@ -20,6 +20,7 @@
     PositionalIndexer,
     Scalar,
     Shape,
+    type_t,
 )
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import (
@@ -135,9 +136,7 @@ def __init__(self, values: np.ndarray, mask: np.ndarray, copy: bool = False):
     def dtype(self) -> BaseMaskedDtype:
         raise AbstractMethodError(self)
 
-    def __getitem__(
-        self, item: Union[int, slice, np.ndarray]
-    ) -> Union[BaseMaskedArray, Any]:
+    def __getitem__(self, item: PositionalIndexer) -> BaseMaskedArray | Any:
         item = check_array_indexer(self, item)
 
         newmask = self._mask[item]
@@ -405,7 +404,7 @@ def nbytes(self) -> int:
 
     @classmethod
     def _concat_same_type(
-        cls: Type[BaseMaskedArrayT],
+        cls: type[BaseMaskedArrayT],
         to_concat: Sequence[BaseMaskedArrayT],
         axis: int = 0,
     ) -> BaseMaskedArrayT:
@@ -418,7 +417,7 @@ def take(
         indexer,
         *,
         allow_fill: bool = False,
-        fill_value: Optional[Scalar] = None,
+        fill_value: Scalar | None = None,
         axis: int = 0,
     ) -> BaseMaskedArrayT:
         # we always fill with 1 internally

pandas/core/arrays/string_.py

@@ -339,7 +339,7 @@ def astype(self, dtype, copy=True):
         return super().astype(dtype, copy)
 
     def _reduce(
-        self, name: str, *, skipna: bool = True, axis: Optional[int] = 0, **kwargs
+        self, name: str, *, skipna: bool = True, axis: int | None = 0, **kwargs
     ):
         if name in ["min", "max"]:
             return getattr(self, name)(skipna=skipna, axis=axis)