Doc: fix pandas.TimedeltaIndex.to_pytimedelta RT03,SA01

ci/code_checks.sh

@@ -70,10 +70,11 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         --format=actions \
         -i ES01 `# For now it is ok if docstrings are missing the extended summary` \
         -i "pandas.Series.dt PR01" `# Accessors are implemented as classes, but we do not document the Parameters section` \
+        -i "pandas.NA SA01" \
         -i "pandas.Period.freq GL08" \
         -i "pandas.Period.ordinal GL08" \
+        -i "pandas.PeriodDtype.freq SA01" \
         -i "pandas.RangeIndex.from_range PR01,SA01" \
-        -i "pandas.RangeIndex.step SA01" \
         -i "pandas.Series.cat.add_categories PR01,PR02" \
         -i "pandas.Series.cat.as_ordered PR01" \
         -i "pandas.Series.cat.as_unordered PR01" \
@@ -97,43 +98,34 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.dt.tz_localize PR01,PR02" \
         -i "pandas.Series.dt.unit GL08" \
         -i "pandas.Series.pad PR01,SA01" \
-        -i "pandas.Series.sparse.fill_value SA01" \
         -i "pandas.Series.sparse.from_coo PR07,SA01" \
         -i "pandas.Series.sparse.npoints SA01" \
-        -i "pandas.Series.sparse.sp_values SA01" \
         -i "pandas.Timedelta.max PR02" \
         -i "pandas.Timedelta.min PR02" \
         -i "pandas.Timedelta.resolution PR02" \
-        -i "pandas.Timedelta.to_timedelta64 SA01" \
-        -i "pandas.TimedeltaIndex.to_pytimedelta RT03,SA01" \
         -i "pandas.Timestamp.max PR02" \
         -i "pandas.Timestamp.min PR02" \
         -i "pandas.Timestamp.nanosecond GL08" \
         -i "pandas.Timestamp.resolution PR02" \
         -i "pandas.Timestamp.tzinfo GL08" \
         -i "pandas.Timestamp.year GL08" \
         -i "pandas.api.types.is_dict_like PR07,SA01" \
+        -i "pandas.api.types.is_extension_array_dtype SA01" \
         -i "pandas.api.types.is_file_like PR07,SA01" \
         -i "pandas.api.types.is_float PR01,SA01" \
-        -i "pandas.api.types.is_float_dtype SA01" \
         -i "pandas.api.types.is_hashable PR01,RT03,SA01" \
-        -i "pandas.api.types.is_int64_dtype SA01" \
         -i "pandas.api.types.is_integer PR01,SA01" \
-        -i "pandas.api.types.is_interval_dtype SA01" \
         -i "pandas.api.types.is_iterator PR07,SA01" \
-        -i "pandas.api.types.is_list_like SA01" \
         -i "pandas.api.types.is_named_tuple PR07,SA01" \
-        -i "pandas.api.types.is_object_dtype SA01" \
         -i "pandas.api.types.is_re PR07,SA01" \
         -i "pandas.api.types.is_re_compilable PR07,SA01" \
         -i "pandas.api.types.pandas_dtype PR07,RT03,SA01" \
         -i "pandas.arrays.ArrowExtensionArray PR07,SA01" \
-        -i "pandas.arrays.BooleanArray SA01" \
         -i "pandas.arrays.DatetimeArray SA01" \
+        -i "pandas.arrays.FloatingArray SA01" \
         -i "pandas.arrays.IntegerArray SA01" \
         -i "pandas.arrays.IntervalArray.left SA01" \
         -i "pandas.arrays.IntervalArray.length SA01" \
-        -i "pandas.arrays.IntervalArray.mid SA01" \
         -i "pandas.arrays.IntervalArray.right SA01" \
         -i "pandas.arrays.NumpyExtensionArray SA01" \
         -i "pandas.arrays.SparseArray PR07,SA01" \
@@ -144,12 +136,16 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.core.groupby.DataFrameGroupBy.boxplot PR07,RT03,SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.get_group RT03,SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.groups SA01" \
+        -i "pandas.core.groupby.DataFrameGroupBy.hist RT03" \
         -i "pandas.core.groupby.DataFrameGroupBy.indices SA01" \
+        -i "pandas.core.groupby.DataFrameGroupBy.max SA01" \
+        -i "pandas.core.groupby.DataFrameGroupBy.min SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.nth PR02" \
         -i "pandas.core.groupby.DataFrameGroupBy.nunique SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.ohlc SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.plot PR02" \
         -i "pandas.core.groupby.DataFrameGroupBy.sem SA01" \
+        -i "pandas.core.groupby.DataFrameGroupBy.sum SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.__iter__ RT03,SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.agg RT03" \
         -i "pandas.core.groupby.SeriesGroupBy.aggregate RT03" \
@@ -158,12 +154,14 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.core.groupby.SeriesGroupBy.indices SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.is_monotonic_decreasing SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.is_monotonic_increasing SA01" \
+        -i "pandas.core.groupby.SeriesGroupBy.max SA01" \
+        -i "pandas.core.groupby.SeriesGroupBy.min SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.nth PR02" \
         -i "pandas.core.groupby.SeriesGroupBy.ohlc SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.plot PR02" \
         -i "pandas.core.groupby.SeriesGroupBy.sem SA01" \
+        -i "pandas.core.groupby.SeriesGroupBy.sum SA01" \
         -i "pandas.core.resample.Resampler.__iter__ RT03,SA01" \
-        -i "pandas.core.resample.Resampler.ffill RT03" \
         -i "pandas.core.resample.Resampler.get_group RT03,SA01" \
         -i "pandas.core.resample.Resampler.groups SA01" \
         -i "pandas.core.resample.Resampler.indices SA01" \
@@ -178,18 +176,17 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.core.resample.Resampler.sum SA01" \
         -i "pandas.core.resample.Resampler.transform PR01,RT03,SA01" \
         -i "pandas.core.resample.Resampler.var SA01" \
-        -i "pandas.date_range RT03" \
         -i "pandas.errors.AttributeConflictWarning SA01" \
         -i "pandas.errors.CSSWarning SA01" \
         -i "pandas.errors.CategoricalConversionWarning SA01" \
         -i "pandas.errors.ChainedAssignmentError SA01" \
         -i "pandas.errors.ClosedFileError SA01" \
         -i "pandas.errors.DataError SA01" \
         -i "pandas.errors.DuplicateLabelError SA01" \
-        -i "pandas.errors.EmptyDataError SA01" \
         -i "pandas.errors.IntCastingNaNError SA01" \
         -i "pandas.errors.InvalidIndexError SA01" \
         -i "pandas.errors.InvalidVersion SA01" \
+        -i "pandas.errors.MergeError SA01" \
         -i "pandas.errors.NullFrequencyError SA01" \
         -i "pandas.errors.NumExprClobberingError SA01" \
         -i "pandas.errors.NumbaUtilError SA01" \
@@ -368,7 +365,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.tseries.offsets.SemiMonthBegin.n GL08" \
         -i "pandas.tseries.offsets.SemiMonthBegin.normalize GL08" \
         -i "pandas.tseries.offsets.SemiMonthBegin.rule_code GL08" \
-        -i "pandas.tseries.offsets.SemiMonthEnd SA01" \
         -i "pandas.tseries.offsets.SemiMonthEnd.day_of_month GL08" \
         -i "pandas.tseries.offsets.SemiMonthEnd.is_on_offset GL08" \
         -i "pandas.tseries.offsets.SemiMonthEnd.n GL08" \
@@ -382,6 +378,7 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.tseries.offsets.Week.n GL08" \
         -i "pandas.tseries.offsets.Week.normalize GL08" \
         -i "pandas.tseries.offsets.Week.weekday GL08" \
+        -i "pandas.tseries.offsets.WeekOfMonth SA01" \
         -i "pandas.tseries.offsets.WeekOfMonth.is_on_offset GL08" \
         -i "pandas.tseries.offsets.WeekOfMonth.n GL08" \
         -i "pandas.tseries.offsets.WeekOfMonth.normalize GL08" \

pandas/_libs/tslibs/timedeltas.pyx

@@ -1189,14 +1189,6 @@ cdef class _Timedelta(timedelta):
         """
         Total seconds in the duration.
 
-        This method calculates the total duration in seconds by combining
-        the days, seconds, and microseconds of the `Timedelta` object.
-
-        See Also
-        --------
-        to_timedelta : Convert argument to timedelta.
-        Timedelta : Represents a duration, the difference between two dates or times.
-
         Examples
         --------
         >>> td = pd.Timedelta('1min')
@@ -1501,17 +1493,6 @@ cdef class _Timedelta(timedelta):
         """
         Return a components namedtuple-like.
 
-        Each component represents a different time unit, allowing you to access the
-        breakdown of the total duration in terms of days, hours, minutes, seconds,
-        milliseconds, microseconds, and nanoseconds.
-
-        See Also
-        --------
-        Timedelta.total_seconds : Returns the total duration of the Timedelta in
-            seconds.
-        to_timedelta : Convert argument to Timedelta.
-        Timedelta : Represents a duration, the difference between two dates or times.
-
         Examples
         --------
         >>> td = pd.Timedelta('2 day 4 min 3 us 42 ns')

pandas/_testing/contexts.py

@@ -73,14 +73,15 @@ def set_timezone(tz: str) -> Generator[None, None, None]:
     import time
 
     def setTZ(tz) -> None:
-        if tz is None:
-            try:
-                del os.environ["TZ"]
-            except KeyError:
-                pass
-        else:
-            os.environ["TZ"] = tz
-            time.tzset()
+        if hasattr(time, "tzset"):
+            if tz is None:
+                try:
+                    del os.environ["TZ"]
+                except KeyError:
+                    pass
+            else:
+                os.environ["TZ"] = tz
+                time.tzset()
 
     orig_tz = os.environ.get("TZ")
     setTZ(tz)

pandas/core/arrays/timedeltas.py

@@ -785,11 +785,24 @@ def total_seconds(self) -> npt.NDArray[np.float64]:
 
     def to_pytimedelta(self) -> npt.NDArray[np.object_]:
         """
-        Return an ndarray of datetime.timedelta objects.
+        Return an ndarray of ``datetime.timedelta`` objects.
 
         Returns
         -------
-        numpy.ndarray
+        **numpy.ndarray**
+            A NumPy ``timedelta64`` object representing the same duration as the
+            original pandas ``Timedelta`` object. The precision of the resulting
+            object is in nanoseconds, which is the default
+            time resolution used by pandas for ``Timedelta`` objects, ensuring
+            high precision for time-based calculations.
+
+        See Also
+        --------
+        to_timedelta : Convert argument to timedelta format.
+        Timedelta : Represents a duration between two dates or times.
+        DatetimeIndex: Index of datetime64 data.
+        Timedelta.components : Return a components namedtuple-like
+                               of a single timedelta.
 
         Examples
         --------
@@ -800,6 +813,14 @@ def to_pytimedelta(self) -> npt.NDArray[np.object_]:
         >>> tdelta_idx.to_pytimedelta()
         array([datetime.timedelta(days=1), datetime.timedelta(days=2),
                datetime.timedelta(days=3)], dtype=object)
+
+        >>> tidx = pd.TimedeltaIndex(data=["1 days 02:30:45", "3 days 04:15:10"])
+        >>> tidx
+        TimedeltaIndex(['1 days 02:30:45', '3 days 04:15:10'],
+               dtype='timedelta64[ns]', freq=None)
+        >>> tidx.to_pytimedelta()
+        array([datetime.timedelta(days=1, seconds=9045),
+                datetime.timedelta(days=3, seconds=15310)], dtype=object)
         """
         return ints_to_pytimedelta(self._ndarray)
 

pandas/tests/tslibs/test_parsing.py

@@ -37,10 +37,13 @@
 )
 def test_parsing_tzlocal_deprecated():
     # GH#50791
-    msg = (
-        r"Parsing 'EST' as tzlocal \(dependent on system timezone\) "
-        r"is no longer supported\. "
-        "Pass the 'tz' keyword or call tz_localize after construction instead"
+    msg = "|".join(
+        [
+            r"Parsing 'EST' as tzlocal \(dependent on system timezone\) "
+            r"is no longer supported\. "
+            "Pass the 'tz' keyword or call tz_localize after construction instead",
+            ".*included an un-recognized timezone",
+        ]
     )
     dtstr = "Jan 15 2004 03:00 EST"