DOC: Refactor _create_delegator_method using functools (#59878)

* add tag dt.to_timestamp, series.rst

* add doc strings for dt.to_timestamp

* update datetimes.py

* refactor _create_delegator_method to use functools wrap

* changes to accessor.py

* remove from code_checks.sh

* update code_checks.sh

* update code_checks.sh

* rewrite functools, adjust unit tests

* update change log

* remove dup entry

* update code_checks.sh

* update

* revert all dt related changes

* update series.rst

* update imports

* format use of functools import

Author: saldanhad

ci/code_checks.sh

@@ -73,27 +73,7 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Period.freq GL08" \
         -i "pandas.Period.ordinal GL08" \
         -i "pandas.RangeIndex.from_range PR01,SA01" \
-        -i "pandas.Series.cat.add_categories PR01,PR02" \
-        -i "pandas.Series.cat.as_ordered PR01" \
-        -i "pandas.Series.cat.as_unordered PR01" \
-        -i "pandas.Series.cat.remove_categories PR01,PR02" \
-        -i "pandas.Series.cat.remove_unused_categories PR01" \
-        -i "pandas.Series.cat.rename_categories PR01,PR02" \
-        -i "pandas.Series.cat.reorder_categories PR01,PR02" \
-        -i "pandas.Series.cat.set_categories PR01,PR02" \
-        -i "pandas.Series.dt.as_unit PR01,PR02" \
-        -i "pandas.Series.dt.ceil PR01,PR02" \
-        -i "pandas.Series.dt.day_name PR01,PR02" \
-        -i "pandas.Series.dt.floor PR01,PR02" \
         -i "pandas.Series.dt.freq GL08" \
-        -i "pandas.Series.dt.month_name PR01,PR02" \
-        -i "pandas.Series.dt.normalize PR01" \
-        -i "pandas.Series.dt.round PR01,PR02" \
-        -i "pandas.Series.dt.strftime PR01,PR02" \
-        -i "pandas.Series.dt.to_period PR01,PR02" \
-        -i "pandas.Series.dt.total_seconds PR01" \
-        -i "pandas.Series.dt.tz_convert PR01,PR02" \
-        -i "pandas.Series.dt.tz_localize PR01,PR02" \
         -i "pandas.Series.dt.unit GL08" \
         -i "pandas.Series.pad PR01,SA01" \
         -i "pandas.Timedelta.max PR02" \

pandas/core/accessor.py

@@ -7,6 +7,7 @@
 
 from __future__ import annotations
 
+import functools
 from typing import (
     TYPE_CHECKING,
     final,
@@ -117,12 +118,12 @@ def _setter(self, new_values):
             )
 
         def _create_delegator_method(name: str):
+            method = getattr(delegate, accessor_mapping(name))
+
+            @functools.wraps(method)
             def f(self, *args, **kwargs):
                 return self._delegate_method(name, *args, **kwargs)
 
-            f.__name__ = name
-            f.__doc__ = getattr(delegate, accessor_mapping(name)).__doc__
-
             return f
 
         for name in accessors:

pandas/core/arrays/categorical.py

@@ -1155,6 +1155,12 @@ def rename_categories(self, new_categories) -> Self:
         """
         Rename categories.
 
+        This method is commonly used to re-label or adjust the
+        category names in categorical data without changing the
+        underlying data. It is useful in situations where you want
+        to modify the labels used for clarity, consistency,
+        or readability.
+
         Parameters
         ----------
         new_categories : list-like, dict-like or callable
@@ -1371,8 +1377,8 @@ def remove_categories(self, removals) -> Self:
         """
         Remove the specified categories.
 
-        `removals` must be included in the old categories. Values which were in
-        the removed categories will be set to NaN
+        The ``removals`` argument must be a subset of the current categories.
+        Any values that were part of the removed categories will be set to NaN.
 
         Parameters
         ----------
@@ -1431,6 +1437,10 @@ def remove_unused_categories(self) -> Self:
         """
         Remove categories which are not used.
 
+        This method is useful when working with datasets
+        that undergo dynamic changes where categories may no longer be
+        relevant, allowing to maintain a clean, efficient data structure.
+
         Returns
         -------
         Categorical