try to fix rest

Author: lithomas1

pandas/core/arrays/numpy_.py

@@ -516,12 +516,15 @@ def to_numpy(
         # to_numpy on StringArray backed by StringDType should still return object dtype
         # for backwards compat
         array = self._ndarray
-        if dtype is None and self._ndarray.dtype.kind == "T":
-            dtype = object
-        result = np.asarray(array, dtype=dtype)
+        if self._ndarray.dtype.kind == "T":
+            array = array.astype(object)
         if na_value is not lib.no_default and mask.any():
-            result = result.copy()
+            result = array.copy()
             result[mask] = na_value
+        else:
+            result = self._ndarray
+
+        result = np.asarray(result, dtype=dtype)
 
         if copy and result is self._ndarray:
             result = result.copy()

pandas/core/nanops.py

@@ -18,7 +18,6 @@
     NaTType,
     iNaT,
     lib,
-    missing as libmissing,
 )
 from pandas._typing import (
     ArrayLike,
@@ -32,7 +31,6 @@
     npt,
 )
 from pandas.compat._optional import import_optional_dependency
-from pandas.compat.numpy import np_version_gt2
 
 from pandas.core.dtypes.common import (
     is_complex,
@@ -153,12 +151,8 @@ def f(
 
 
 def _bn_ok_dtype(dtype: DtypeObj, name: str) -> bool:
-    # Bottleneck chokes on datetime64, PeriodDtype (or and EA)
-    if (
-        dtype != object
-        and (np_version_gt2 and dtype != np.dtypes.StringDType(na_object=libmissing.NA))
-        and not needs_i8_conversion(dtype)
-    ):
+    # Bottleneck chokes on datetime64, numpy strins, PeriodDtype (or and EA)
+    if dtype != object and dtype.kind != "T" and not needs_i8_conversion(dtype):
         # GH 42878
         # Bottleneck uses naive summation leading to O(n) loss of precision
         # unlike numpy which implements pairwise summation, which has O(log(n)) loss