diff --git a/ci/code_checks.sh b/ci/code_checks.sh index cabc25b5e0ba5..9422d4f47a538 100755 --- a/ci/code_checks.sh +++ b/ci/code_checks.sh @@ -107,22 +107,16 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then -i "pandas.DataFrame.to_markdown SA01" \ -i "pandas.DataFrame.to_parquet RT03" \ -i "pandas.DataFrame.var PR01,RT03,SA01" \ - -i "pandas.DatetimeIndex.ceil SA01" \ -i "pandas.DatetimeIndex.date SA01" \ -i "pandas.DatetimeIndex.day_of_year SA01" \ -i "pandas.DatetimeIndex.dayofyear SA01" \ - -i "pandas.DatetimeIndex.floor SA01" \ -i "pandas.DatetimeIndex.freqstr SA01" \ -i "pandas.DatetimeIndex.indexer_at_time PR01,RT03" \ -i "pandas.DatetimeIndex.indexer_between_time RT03" \ -i "pandas.DatetimeIndex.inferred_freq SA01" \ -i "pandas.DatetimeIndex.is_leap_year SA01" \ - -i "pandas.DatetimeIndex.quarter SA01" \ - -i "pandas.DatetimeIndex.round SA01" \ - -i "pandas.DatetimeIndex.snap PR01,RT03,SA01" \ + -i "pandas.DatetimeIndex.snap PR01,RT03" \ -i "pandas.DatetimeIndex.std PR01,RT03" \ - -i "pandas.DatetimeIndex.time SA01" \ - -i "pandas.DatetimeIndex.timetz SA01" \ -i "pandas.DatetimeIndex.to_period RT03" \ -i "pandas.DatetimeIndex.to_pydatetime RT03,SA01" \ -i "pandas.DatetimeIndex.tz SA01" \ @@ -289,7 +283,7 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then -i "pandas.Series.div PR07" \ -i "pandas.Series.droplevel SA01" \ -i "pandas.Series.dt.as_unit PR01,PR02" \ - -i "pandas.Series.dt.ceil PR01,PR02,SA01" \ + -i "pandas.Series.dt.ceil PR01,PR02" \ -i "pandas.Series.dt.components SA01" \ -i "pandas.Series.dt.date SA01" \ -i "pandas.Series.dt.day_name PR01,PR02" \ @@ -298,20 +292,17 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then -i "pandas.Series.dt.days SA01" \ -i "pandas.Series.dt.days_in_month SA01" \ -i "pandas.Series.dt.daysinmonth SA01" \ - -i "pandas.Series.dt.floor PR01,PR02,SA01" \ + -i "pandas.Series.dt.floor PR01,PR02" \ -i "pandas.Series.dt.freq GL08" \ -i "pandas.Series.dt.is_leap_year SA01" \ -i "pandas.Series.dt.microseconds SA01" \ -i "pandas.Series.dt.month_name PR01,PR02" \ -i "pandas.Series.dt.nanoseconds SA01" \ -i "pandas.Series.dt.normalize PR01" \ - -i "pandas.Series.dt.quarter SA01" \ -i "pandas.Series.dt.qyear GL08" \ - -i "pandas.Series.dt.round PR01,PR02,SA01" \ + -i "pandas.Series.dt.round PR01,PR02" \ -i "pandas.Series.dt.seconds SA01" \ -i "pandas.Series.dt.strftime PR01,PR02" \ - -i "pandas.Series.dt.time SA01" \ - -i "pandas.Series.dt.timetz SA01" \ -i "pandas.Series.dt.to_period PR01,PR02,RT03" \ -i "pandas.Series.dt.total_seconds PR01" \ -i "pandas.Series.dt.tz SA01" \ @@ -432,14 +423,11 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then -i "pandas.Timedelta.total_seconds SA01" \ -i "pandas.Timedelta.view SA01" \ -i "pandas.TimedeltaIndex.as_unit RT03,SA01" \ - -i "pandas.TimedeltaIndex.ceil SA01" \ -i "pandas.TimedeltaIndex.components SA01" \ -i "pandas.TimedeltaIndex.days SA01" \ - -i "pandas.TimedeltaIndex.floor SA01" \ -i "pandas.TimedeltaIndex.inferred_freq SA01" \ -i "pandas.TimedeltaIndex.microseconds SA01" \ -i "pandas.TimedeltaIndex.nanoseconds SA01" \ - -i "pandas.TimedeltaIndex.round SA01" \ -i "pandas.TimedeltaIndex.seconds SA01" \ -i "pandas.TimedeltaIndex.to_pytimedelta RT03,SA01" \ -i "pandas.Timestamp PR07,SA01" \ diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py index 8ada9d88e08bc..974289160b145 100644 --- a/pandas/core/arrays/datetimelike.py +++ b/pandas/core/arrays/datetimelike.py @@ -1825,6 +1825,11 @@ 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 diff --git a/pandas/core/arrays/datetimes.py b/pandas/core/arrays/datetimes.py index 7704c99141fc2..fb9f047d432a1 100644 --- a/pandas/core/arrays/datetimes.py +++ b/pandas/core/arrays/datetimes.py @@ -1391,6 +1391,14 @@ def time(self) -> npt.NDArray[np.object_]: The time part of the Timestamps. + See Also + -------- + DatetimeIndex.timetz : Returns numpy array of :class:`datetime.time` + objects with timezones. The time part of the Timestamps. + DatetimeIndex.date : Returns numpy array of python :class:`datetime.date` + objects. Namely, the date part of Timestamps without time and timezone + information. + Examples -------- For Series: @@ -1428,6 +1436,12 @@ def timetz(self) -> npt.NDArray[np.object_]: The time part of the Timestamps. + See Also + -------- + DatetimeIndex.time : Returns numpy array of :class:`datetime.time` objects. + The time part of the Timestamps. + DatetimeIndex.tz : Return the timezone. + Examples -------- For Series: @@ -1836,6 +1850,12 @@ def isocalendar(self) -> DataFrame: """ The quarter of the date. + See Also + -------- + DatetimeIndex.snap : Snap time stamps to nearest occurring frequency. + DatetimeIndex.time : Returns numpy array of datetime.time objects. + The time part of the Timestamps. + Examples -------- For Series: diff --git a/pandas/core/indexes/datetimes.py b/pandas/core/indexes/datetimes.py index cefdc14145d1f..7122de745e13b 100644 --- a/pandas/core/indexes/datetimes.py +++ b/pandas/core/indexes/datetimes.py @@ -455,6 +455,13 @@ def snap(self, freq: Frequency = "S") -> DatetimeIndex: ------- DatetimeIndex + See Also + -------- + DatetimeIndex.round : Perform round operation on the data to the + specified `freq`. + DatetimeIndex.floor : Perform floor operation on the data to the + specified `freq`. + Examples -------- >>> idx = pd.DatetimeIndex(