CLN: remove unnecessary Series._convert calls

pandas/core/generic.py

@@ -5700,7 +5700,6 @@ def _convert(
         numeric: bool_t = False,
         timedelta: bool_t = False,
         coerce: bool_t = False,
-        copy: bool_t = True,
     ) -> FrameOrSeries:
         """
         Attempt to infer better dtype for object columns
@@ -5717,10 +5716,6 @@ def _convert(
         coerce : bool, default False
             If True, force conversion with unconvertible values converted to
             nulls (NaN or NaT).
-        copy : bool, default True
-            If True, return a copy even if no copy is necessary (e.g. no
-            conversion was done). Note: This is meant for internal use, and
-            should not be confused with inplace.
 
         Returns
         -------
@@ -5730,14 +5725,13 @@ def _convert(
         validate_bool_kwarg(numeric, "numeric")
         validate_bool_kwarg(timedelta, "timedelta")
         validate_bool_kwarg(coerce, "coerce")
-        validate_bool_kwarg(copy, "copy")
         return self._constructor(
             self._data.convert(
                 datetime=datetime,
                 numeric=numeric,
                 timedelta=timedelta,
                 coerce=coerce,
-                copy=copy,
+                copy=True,
             )
         ).__finalize__(self)
 

pandas/core/groupby/generic.py

@@ -29,7 +29,7 @@
 
 import numpy as np
 
-from pandas._libs import Timestamp, lib
+from pandas._libs import lib
 from pandas._typing import FrameOrSeries
 from pandas.util._decorators import Appender, Substitution
 
@@ -386,7 +386,7 @@ def _wrap_aggregated_output(
         result = self._wrap_series_output(
             output=output, index=self.grouper.result_index
         )
-        return self._reindex_output(result)._convert(datetime=True)
+        return self._reindex_output(result)
 
     def _wrap_transformed_output(
         self, output: Mapping[base.OutputKey, Union[Series, np.ndarray]]
@@ -1342,14 +1342,10 @@ def first_not_none(values):
 
             # values are not series or array-like but scalars
             else:
-                # only coerce dates if we find at least 1 datetime
-                should_coerce = any(isinstance(x, Timestamp) for x in values)
                 # self._selection_name not passed through to Series as the
                 # result should not take the name of original selection
                 # of columns
-                return Series(values, index=key_index)._convert(
-                    datetime=True, coerce=should_coerce
-                )
+                return Series(values, index=key_index)
 
         else:
             # Handle cases like BinGrouper
@@ -1699,7 +1695,7 @@ def _wrap_aggregated_output(
         if self.axis == 1:
             result = result.T
 
-        return self._reindex_output(result)._convert(datetime=True)
+        return self._reindex_output(result)
 
     def _wrap_transformed_output(
         self, output: Mapping[base.OutputKey, Union[Series, np.ndarray]]