REF (string): de-duplicate str_map_nan_semantics (pandas-dev#59464)

REF: de-duplicate str_map_nan_semantics

pandas/core/arrays/string_.py

@@ -391,7 +391,7 @@ def _str_map(
             return constructor(result, mask)
 
         else:
-            return self._str_map_str_or_object(dtype, na_value, arr, f, mask, convert)
+            return self._str_map_str_or_object(dtype, na_value, arr, f, mask)
 
     def _str_map_str_or_object(
         self,
@@ -400,7 +400,6 @@ def _str_map_str_or_object(
         arr: np.ndarray,
         f,
         mask: npt.NDArray[np.bool_],
-        convert: bool,
     ):
         # _str_map helper for case where dtype is either string dtype or object
         if is_string_dtype(dtype) and not is_object_dtype(dtype):
@@ -434,7 +433,6 @@ def _str_map_nan_semantics(
 
         mask = isna(self)
         arr = np.asarray(self)
-        convert = convert and not np.all(mask)
 
         if is_integer_dtype(dtype) or is_bool_dtype(dtype):
             na_value_is_na = isna(na_value)
@@ -453,6 +451,9 @@ def _str_map_nan_semantics(
                 dtype=np.dtype(cast(type, dtype)),
             )
             if na_value_is_na and mask.any():
+                # TODO: we could alternatively do this check before map_infer_mask
+                #  and adjust the dtype/na_value we pass there. Which is more
+                #  performant?
                 if is_integer_dtype(dtype):
                     result = result.astype("float64")
                 else:
@@ -461,7 +462,7 @@ def _str_map_nan_semantics(
             return result
 
         else:
-            return self._str_map_str_or_object(dtype, na_value, arr, f, mask, convert)
+            return self._str_map_str_or_object(dtype, na_value, arr, f, mask)
 
 
 # error: Definition of "_concat_same_type" in base class "NDArrayBacked" is

pandas/core/arrays/string_arrow.py

@@ -7,7 +7,6 @@
     TYPE_CHECKING,
     Callable,
     Union,
-    cast,
 )
 import warnings
 
@@ -24,8 +23,6 @@
 from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.common import (
-    is_bool_dtype,
-    is_integer_dtype,
     is_scalar,
     pandas_dtype,
 )
@@ -285,45 +282,6 @@ def _data(self):
 
     _str_map = BaseStringArray._str_map
 
-    def _str_map_nan_semantics(
-        self, f, na_value=None, dtype: Dtype | None = None, convert: bool = True
-    ):
-        if dtype is None:
-            dtype = self.dtype
-        if na_value is None:
-            na_value = self.dtype.na_value
-
-        mask = isna(self)
-        arr = np.asarray(self)
-
-        if is_integer_dtype(dtype) or is_bool_dtype(dtype):
-            if is_integer_dtype(dtype):
-                na_value = np.nan
-            else:
-                na_value = False
-
-            dtype = np.dtype(cast(type, dtype))
-            if mask.any():
-                # numpy int/bool dtypes cannot hold NaNs so we must convert to
-                # float64 for int (to match maybe_convert_objects) or
-                # object for bool (again to match maybe_convert_objects)
-                if is_integer_dtype(dtype):
-                    dtype = np.dtype("float64")
-                else:
-                    dtype = np.dtype(object)
-            result = lib.map_infer_mask(
-                arr,
-                f,
-                mask.view("uint8"),
-                convert=False,
-                na_value=na_value,
-                dtype=dtype,
-            )
-            return result
-
-        else:
-            return self._str_map_str_or_object(dtype, na_value, arr, f, mask, convert)
-
     def _str_contains(
         self, pat, case: bool = True, flags: int = 0, na=np.nan, regex: bool = True
     ):