REF: remove unused variable and checks (#59463)

* REF: removed unused convert keyword

* REF: remove unhit field checks

* REF: inline _object_ops

Author: jbrockmendel

pandas/core/arrays/datetimes.py

@@ -158,15 +158,8 @@ def f(self):
             # these return a boolean by-definition
             return result
 
-        if field in self._object_ops:
-            result = fields.get_date_name_field(values, field, reso=self._creso)
-            result = self._maybe_mask_results(result, fill_value=None)
-
-        else:
-            result = fields.get_date_field(values, field, reso=self._creso)
-            result = self._maybe_mask_results(
-                result, fill_value=None, convert="float64"
-            )
+        result = fields.get_date_field(values, field, reso=self._creso)
+        result = self._maybe_mask_results(result, fill_value=None, convert="float64")
 
         return result
 
@@ -243,7 +236,6 @@ def _scalar_type(self) -> type[Timestamp]:
         "is_year_end",
         "is_leap_year",
     ]
-    _object_ops: list[str] = ["freq", "tz"]
     _field_ops: list[str] = [
         "year",
         "month",
@@ -264,7 +256,7 @@ def _scalar_type(self) -> type[Timestamp]:
     ]
     _other_ops: list[str] = ["date", "time", "timetz"]
     _datetimelike_ops: list[str] = (
-        _field_ops + _object_ops + _bool_ops + _other_ops + ["unit"]
+        _field_ops + _bool_ops + _other_ops + ["unit", "freq", "tz"]
     )
     _datetimelike_methods: list[str] = [
         "to_period",

pandas/core/arrays/string_.py

@@ -354,9 +354,7 @@ def _str_map(
         self, f, na_value=None, dtype: Dtype | None = None, convert: bool = True
     ):
         if self.dtype.na_value is np.nan:
-            return self._str_map_nan_semantics(
-                f, na_value=na_value, dtype=dtype, convert=convert
-            )
+            return self._str_map_nan_semantics(f, na_value=na_value, dtype=dtype)
 
         from pandas.arrays import BooleanArray
 
@@ -431,9 +429,7 @@ def _str_map_str_or_object(
             # -> We don't know the result type. E.g. `.get` can return anything.
             return lib.map_infer_mask(arr, f, mask.view("uint8"))
 
-    def _str_map_nan_semantics(
-        self, f, na_value=None, dtype: Dtype | None = None, convert: bool = True
-    ):
+    def _str_map_nan_semantics(self, f, na_value=None, dtype: Dtype | None = None):
         if dtype is None:
             dtype = self.dtype
         if na_value is None:

pandas/core/arrays/timedeltas.py

@@ -152,9 +152,8 @@ def _scalar_type(self) -> type[Timedelta]:
     # define my properties & methods for delegation
     _other_ops: list[str] = []
     _bool_ops: list[str] = []
-    _object_ops: list[str] = ["freq"]
     _field_ops: list[str] = ["days", "seconds", "microseconds", "nanoseconds"]
-    _datetimelike_ops: list[str] = _field_ops + _object_ops + _bool_ops + ["unit"]
+    _datetimelike_ops: list[str] = _field_ops + _bool_ops + ["unit", "freq"]
     _datetimelike_methods: list[str] = [
         "to_pytimedelta",
         "total_seconds",