CLN: assorted follow-ups

doc/source/whatsnew/v1.3.0.rst

@@ -643,6 +643,7 @@ Other API changes
 - Partially initialized :class:`CategoricalDtype` (i.e. those with ``categories=None`` objects will no longer compare as equal to fully initialized dtype objects.
 - Accessing ``_constructor_expanddim`` on a :class:`DataFrame` and ``_constructor_sliced`` on a :class:`Series` now raise an ``AttributeError``. Previously a ``NotImplementedError`` was raised (:issue:`38782`)
 - Added new ``engine`` and ``**engine_kwargs`` parameters to :meth:`DataFrame.to_sql` to support other future "SQL engines". Currently we still only use ``SQLAlchemy`` under the hood, but more engines are planned to be supported such as ``turbodbc`` (:issue:`36893`)
+- Removed redundant ``freq`` from :class:`PeriodIndex` string representation (:issue:`41653`)
 
 Build
 =====

pandas/_libs/lib.pyx

@@ -1461,7 +1461,7 @@ def infer_dtype(value: object, skipna: bool = True) -> str:
     for i in range(n):
         val = values[i]
 
-        # do not use is_nul_datetimelike to keep
+        # do not use is_null_datetimelike to keep
         # np.datetime64('nat') and np.timedelta64('nat')
         if val is None or util.is_nan(val):
             pass

pandas/core/algorithms.py

@@ -1266,14 +1266,14 @@ def compute(self, method: str) -> Series:
             return dropped.sort_values(ascending=ascending).head(n)
 
         # fast method
-        arr, pandas_dtype = _ensure_data(dropped.values)
+        arr, new_dtype = _ensure_data(dropped.values)
         if method == "nlargest":
             arr = -arr
-            if is_integer_dtype(pandas_dtype):
+            if is_integer_dtype(new_dtype):
                 # GH 21426: ensure reverse ordering at boundaries
                 arr -= 1
 
-            elif is_bool_dtype(pandas_dtype):
+            elif is_bool_dtype(new_dtype):
                 # GH 26154: ensure False is smaller than True
                 arr = 1 - (-arr)
 

pandas/core/arrays/datetimes.py

@@ -2104,7 +2104,6 @@ def sequence_to_dt64ns(
         result = data.view(DT64NS_DTYPE)
 
     if copy:
-        # TODO: should this be deepcopy?
         result = result.copy()
 
     assert isinstance(result, np.ndarray), type(result)

pandas/core/arrays/period.py

@@ -866,7 +866,7 @@ def start_time(self) -> DatetimeArray:
     def end_time(self) -> DatetimeArray:
         return self.to_timestamp(how="end")
 
-    def _require_matching_freq(self, other, base=False):
+    def _require_matching_freq(self, other, base: bool = False) -> None:
         # See also arrays.period.raise_on_incompatible
         if isinstance(other, BaseOffset):
             other_freq = other
@@ -1057,7 +1057,7 @@ def dt64arr_to_periodarr(data, freq, tz=None):
 
     Returns
     -------
-    ordinals : ndarray[int]
+    ordinals : ndarray[int64]
     freq : Tick
         The frequency extracted from the Series or DatetimeIndex if that's
         used.

pandas/core/indexes/base.py

@@ -776,6 +776,7 @@ def _engine(self) -> libindex.IndexEngine:
         target_values = self._get_engine_target()
         return self._engine_type(lambda: target_values, len(self))
 
+    @final
     @cache_readonly
     def _dir_additions_for_owner(self) -> set[str_t]:
         """
@@ -6209,6 +6210,7 @@ def shape(self) -> Shape:
         # See GH#27775, GH#27384 for history/reasoning in how this is defined.
         return (len(self),)
 
+    @final
     def _deprecated_arg(self, value, name: str_t, methodname: str_t) -> None:
         """
         Issue a FutureWarning if the arg/kwarg is not no_default.