add description to whatsnew and revert unwanted changes in datetimearray docstring

make pre-commit happy

Author: annika-rudolph

doc/source/whatsnew/v3.0.0.rst

@@ -549,6 +549,7 @@ MultiIndex
 - :func:`DataFrame.loc` with ``axis=0``  and :class:`MultiIndex` when setting a value adds extra columns (:issue:`58116`)
 - :meth:`DataFrame.melt` would not accept multiple names in ``var_name`` when the columns were a :class:`MultiIndex` (:issue:`58033`)
 - :meth:`MultiIndex.insert` would not insert NA value correctly at unified location of index -1 (:issue:`59003`)
+- :func:`MultiIndex.get_level_values` accessing a :class:`DatetimeIndex` does not carry the frequency attribute along (:issue:`58327`, :issue:`57949`)
 -
 
 I/O

pandas/core/arrays/datetimelike.py

@@ -1769,7 +1769,7 @@ def strftime(self, date_format: str) -> npt.NDArray[np.object_]:
     ----------
     freq : str or Offset
         The frequency level to {op} the index to. Must be a fixed
-        frequency like 'S' (second) not 'ME' (month end). See
+        frequency like 's' (second) not 'ME' (month end). See
         :ref:`frequency aliases <timeseries.offset_aliases>` for
         a list of possible `freq` values.
     ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'
@@ -1807,6 +1807,11 @@ def strftime(self, date_format: str) -> npt.NDArray[np.object_]:
     ------
     ValueError if the `freq` cannot be converted.
 
+    See Also
+    --------
+    DatetimeIndex.floor : Perform floor operation on the data to the specified `freq`.
+    DatetimeIndex.snap : Snap time stamps to nearest occurring frequency.
+
     Notes
     -----
     If the timestamps have a timezone, {op}ing will take place relative to the

pandas/tests/indexes/multi/test_get_level_values.py

@@ -126,9 +126,7 @@ def test_values_loses_freq_of_underlying_index():
 
 def test_get_level_values_gets_frequency_correctly():
     # GH#57949 GH#58327
-    datetime_index = pd.date_range(
-        start=pd.to_datetime("1/1/2018"), periods=4, freq="YS"
-    )
+    datetime_index = date_range(start=pd.to_datetime("1/1/2018"), periods=4, freq="YS")
     other_index = ["A"]
     multi_index = MultiIndex.from_product([datetime_index, other_index])