fixes for merge with master

Author: Dr-Irv

pandas/core/algorithms.py

@@ -32,7 +32,6 @@
     AnyArrayLike,
     AnySequenceLike,
     ArrayLike,
-    Dtype,
     DtypeObj,
     FrameOrSeriesUnion,
 )
@@ -218,7 +217,7 @@ def _ensure_data(values: ArrayLike) -> Tuple[np.ndarray, DtypeObj]:
 
 
 def _reconstruct_data(
-    values: ArrayLike, dtype: Dtype, original: AnyArrayLike
+    values: ArrayLike, dtype: DtypeObj, original: AnyArrayLike
 ) -> ArrayLike:
     """
     reverse of _ensure_data

pandas/core/arrays/datetimelike.py

@@ -124,6 +124,8 @@
 from pandas.tseries import frequencies
 
 if TYPE_CHECKING:
+    from typing import Literal
+
     from pandas.core.arrays import (
         DatetimeArray,
         TimedeltaArray,
@@ -460,9 +462,15 @@ def view(self: DatetimeLikeArrayT) -> DatetimeLikeArrayT:
         ...
 
     @overload
-    def view(
-        self: DatetimeLikeArrayT, dtype: Optional[Dtype] = ...
-    ) -> DatetimeLikeArrayT:
+    def view(self, dtype: Literal["M8[ns]"]) -> DatetimeArray:
+        ...
+
+    @overload
+    def view(self, dtype: Literal["m8[ns]"]) -> TimedeltaArray:
+        ...
+
+    @overload
+    def view(self, dtype: Optional[Dtype] = ...) -> ArrayLike:
         ...
 
     def view(self, dtype: Optional[Dtype] = None) -> ArrayLike:

pandas/core/arrays/sparse/dtype.py

@@ -27,7 +27,6 @@
 from pandas.core.dtypes.cast import astype_nansafe
 from pandas.core.dtypes.common import (
     is_bool_dtype,
-    is_extension_array_dtype,
     is_object_dtype,
     is_scalar,
     is_string_dtype,
@@ -339,14 +338,10 @@ def update_dtype(self, dtype):
         dtype = pandas_dtype(dtype)
 
         if not isinstance(dtype, cls):
-            if is_extension_array_dtype(dtype):
+            if not isinstance(dtype, np.dtype):
                 raise TypeError("sparse arrays of extension dtypes not supported")
 
-            # error: Item "ExtensionArray" of "Union[ExtensionArray, ndarray]" has no
-            # attribute "item"
-            fill_value = astype_nansafe(  # type: ignore[union-attr]
-                np.array(self.fill_value), dtype
-            ).item()
+            fill_value = astype_nansafe(np.array(self.fill_value), dtype).item()
             dtype = cls(dtype, fill_value=fill_value)
 
         return dtype

pandas/core/dtypes/cast.py

@@ -25,6 +25,7 @@
     Type,
     Union,
     cast,
+    overload,
 )
 import warnings
 
@@ -107,6 +108,8 @@
 )
 
 if TYPE_CHECKING:
+    from typing import Literal
+
     from pandas import Series
     from pandas.core.arrays import (
         DatetimeArray,
@@ -1164,6 +1167,20 @@ def astype_td64_unit_conversion(
     return result
 
 
+@overload
+def astype_nansafe(
+    arr: np.ndarray, dtype: np.dtype, copy: bool = ..., skipna: bool = ...
+) -> np.ndarray:
+    ...
+
+
+@overload
+def astype_nansafe(
+    arr: np.ndarray, dtype: ExtensionDtype, copy: bool = ..., skipna: bool = ...
+) -> ExtensionArray:
+    ...
+
+
 def astype_nansafe(
     arr: np.ndarray, dtype: DtypeObj, copy: bool = True, skipna: bool = False
 ) -> ArrayLike:
@@ -1190,14 +1207,10 @@ def astype_nansafe(
         flags = arr.flags
         flat = arr.ravel("K")
         result = astype_nansafe(flat, dtype, copy=copy, skipna=skipna)
-        order = "F" if flags.f_contiguous else "C"
+        order: Literal["C", "F"] = "F" if flags.f_contiguous else "C"
         # error: Item "ExtensionArray" of "Union[ExtensionArray, ndarray]" has no
         # attribute "reshape"
-        # error: No overload variant of "reshape" of "_ArrayOrScalarCommon" matches
-        # argument types "Tuple[int, ...]", "str"
-        return result.reshape(  # type: ignore[union-attr,call-overload]
-            arr.shape, order=order
-        )
+        return result.reshape(arr.shape, order=order)  # type: ignore[union-attr]
 
     # We get here with 0-dim from sparse
     arr = np.atleast_1d(arr)

pandas/io/formats/format.py

@@ -1797,7 +1797,7 @@ def get_format_timedelta64(
 
     If box, then show the return in quotes
     """
-    values_int = values.view(np.int64)
+    values_int = cast(np.ndarray, values.view(np.int64))
 
     consider_values = values_int != iNaT