diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py index c68b329b00968..04b1bb5ae1398 100644 --- a/pandas/core/arrays/datetimelike.py +++ b/pandas/core/arrays/datetimelike.py @@ -81,8 +81,6 @@ PerformanceWarning, ) from pandas.util._decorators import ( - Appender, - Substitution, cache_readonly, ) from pandas.util._exceptions import find_stack_level @@ -1766,10 +1764,6 @@ class DatelikeOps(DatetimeLikeArrayMixin): Common ops for DatetimeIndex/PeriodIndex, but not TimedeltaIndex. """ - @Substitution( - URL="https://docs.python.org/3/library/datetime.html" - "#strftime-and-strptime-behavior" - ) def strftime(self, date_format: str) -> npt.NDArray[np.object_]: """ Convert to Index using specified date_format. @@ -1777,7 +1771,7 @@ def strftime(self, date_format: str) -> npt.NDArray[np.object_]: Return an Index of formatted strings specified by date_format, which supports the same string format as the python standard library. Details of the string format can be found in `python string format - doc <%(URL)s>`__. + doc `__. Formats supported by the C `strftime` API but not by the python string format doc (such as `"%%R"`, `"%%r"`) are not officially supported and should be @@ -1822,156 +1816,6 @@ def strftime(self, date_format: str) -> npt.NDArray[np.object_]: return result.astype(object, copy=False) -_round_doc = """ - Perform {op} operation on the data to the specified `freq`. - - Parameters - ---------- - 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 - :ref:`frequency aliases ` for - a list of possible `freq` values. - ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise' - Only relevant for DatetimeIndex: - - - 'infer' will attempt to infer fall dst-transition hours based on - order - - bool-ndarray where True signifies a DST time, False designates - a non-DST time (note that this flag is only applicable for - ambiguous times) - - 'NaT' will return NaT where there are ambiguous times - - 'raise' will raise a ValueError if there are ambiguous - times. - - nonexistent : 'shift_forward', 'shift_backward', 'NaT', timedelta, default 'raise' - A nonexistent time does not exist in a particular timezone - where clocks moved forward due to DST. - - - 'shift_forward' will shift the nonexistent time forward to the - closest existing time - - 'shift_backward' will shift the nonexistent time backward to the - closest existing time - - 'NaT' will return NaT where there are nonexistent times - - timedelta objects will shift nonexistent times by the timedelta - - 'raise' will raise a ValueError if there are - nonexistent times. - - Returns - ------- - DatetimeIndex, TimedeltaIndex, or Series - Index of the same type for a DatetimeIndex or TimedeltaIndex, - or a Series with the same index for a Series. - - Raises - ------ - 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 - local ("wall") time and re-localized to the same timezone. When {op}ing - near daylight savings time, use ``nonexistent`` and ``ambiguous`` to - control the re-localization behavior. - - Examples - -------- - **DatetimeIndex** - - >>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min') - >>> rng - DatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00', - '2018-01-01 12:01:00'], - dtype='datetime64[ns]', freq='min') - """ - -_round_example = """>>> rng.round('h') - DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00', - '2018-01-01 12:00:00'], - dtype='datetime64[ns]', freq=None) - - **Series** - - >>> pd.Series(rng).dt.round("h") - 0 2018-01-01 12:00:00 - 1 2018-01-01 12:00:00 - 2 2018-01-01 12:00:00 - dtype: datetime64[ns] - - When rounding near a daylight savings time transition, use ``ambiguous`` or - ``nonexistent`` to control how the timestamp should be re-localized. - - >>> rng_tz = pd.DatetimeIndex(["2021-10-31 03:30:00"], tz="Europe/Amsterdam") - - >>> rng_tz.floor("2h", ambiguous=False) - DatetimeIndex(['2021-10-31 02:00:00+01:00'], - dtype='datetime64[s, Europe/Amsterdam]', freq=None) - - >>> rng_tz.floor("2h", ambiguous=True) - DatetimeIndex(['2021-10-31 02:00:00+02:00'], - dtype='datetime64[s, Europe/Amsterdam]', freq=None) - """ - -_floor_example = """>>> rng.floor('h') - DatetimeIndex(['2018-01-01 11:00:00', '2018-01-01 12:00:00', - '2018-01-01 12:00:00'], - dtype='datetime64[ns]', freq=None) - - **Series** - - >>> pd.Series(rng).dt.floor("h") - 0 2018-01-01 11:00:00 - 1 2018-01-01 12:00:00 - 2 2018-01-01 12:00:00 - dtype: datetime64[ns] - - When rounding near a daylight savings time transition, use ``ambiguous`` or - ``nonexistent`` to control how the timestamp should be re-localized. - - >>> rng_tz = pd.DatetimeIndex(["2021-10-31 03:30:00"], tz="Europe/Amsterdam") - - >>> rng_tz.floor("2h", ambiguous=False) - DatetimeIndex(['2021-10-31 02:00:00+01:00'], - dtype='datetime64[s, Europe/Amsterdam]', freq=None) - - >>> rng_tz.floor("2h", ambiguous=True) - DatetimeIndex(['2021-10-31 02:00:00+02:00'], - dtype='datetime64[s, Europe/Amsterdam]', freq=None) - """ - -_ceil_example = """>>> rng.ceil('h') - DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00', - '2018-01-01 13:00:00'], - dtype='datetime64[ns]', freq=None) - - **Series** - - >>> pd.Series(rng).dt.ceil("h") - 0 2018-01-01 12:00:00 - 1 2018-01-01 12:00:00 - 2 2018-01-01 13:00:00 - dtype: datetime64[ns] - - When rounding near a daylight savings time transition, use ``ambiguous`` or - ``nonexistent`` to control how the timestamp should be re-localized. - - >>> rng_tz = pd.DatetimeIndex(["2021-10-31 01:30:00"], tz="Europe/Amsterdam") - - >>> rng_tz.ceil("h", ambiguous=False) - DatetimeIndex(['2021-10-31 02:00:00+01:00'], - dtype='datetime64[s, Europe/Amsterdam]', freq=None) - - >>> rng_tz.ceil("h", ambiguous=True) - DatetimeIndex(['2021-10-31 02:00:00+02:00'], - dtype='datetime64[s, Europe/Amsterdam]', freq=None) - """ - - class TimelikeOps(DatetimeLikeArrayMixin): """ Common ops for TimedeltaIndex/DatetimeIndex, but not PeriodIndex. @@ -2250,31 +2094,310 @@ def _round(self, freq, mode, ambiguous, nonexistent): result = result.view(self._ndarray.dtype) return self._simple_new(result, dtype=self.dtype) - @Appender((_round_doc + _round_example).format(op="round")) def round( self, freq, ambiguous: TimeAmbiguous = "raise", nonexistent: TimeNonexistent = "raise", ) -> Self: + """ + Perform round operation on the data to the specified `freq`. + + Parameters + ---------- + freq : str or Offset + The frequency level to round the index to. Must be a fixed + frequency like 's' (second) not 'ME' (month end). See + :ref:`frequency aliases ` for + a list of possible `freq` values. + ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise' + Only relevant for DatetimeIndex: + + - 'infer' will attempt to infer fall dst-transition hours based on + order + - bool-ndarray where True signifies a DST time, False designates + a non-DST time (note that this flag is only applicable for + ambiguous times) + - 'NaT' will return NaT where there are ambiguous times + - 'raise' will raise a ValueError if there are ambiguous + times. + + nonexistent : 'shift_forward', 'shift_backward', 'NaT', timedelta, + default 'raise' + A nonexistent time does not exist in a particular timezone + where clocks moved forward due to DST. + + - 'shift_forward' will shift the nonexistent time forward to the + closest existing time + - 'shift_backward' will shift the nonexistent time backward to the + closest existing time + - 'NaT' will return NaT where there are nonexistent times + - timedelta objects will shift nonexistent times by the timedelta + - 'raise' will raise a ValueError if there are + nonexistent times. + + Returns + ------- + DatetimeIndex, TimedeltaIndex, or Series + Index of the same type for a DatetimeIndex or TimedeltaIndex, + or a Series with the same index for a Series. + + Raises + ------ + 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, rounding will take place relative to the + local ("wall") time and re-localized to the same timezone. When rounding + near daylight savings time, use ``nonexistent`` and ``ambiguous`` to + control the re-localization behavior. + + Examples + -------- + **DatetimeIndex** + + >>> rng = pd.date_range("1/1/2018 11:59:00", periods=3, freq="min") + >>> rng + DatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00', + '2018-01-01 12:01:00'], + dtype='datetime64[ns]', freq='min') + >>> rng.round("h") + DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00', + '2018-01-01 12:00:00'], + dtype='datetime64[ns]', freq=None) + + **Series** + + >>> pd.Series(rng).dt.round("h") + 0 2018-01-01 12:00:00 + 1 2018-01-01 12:00:00 + 2 2018-01-01 12:00:00 + dtype: datetime64[ns] + + When rounding near a daylight savings time transition, use ``ambiguous`` or + ``nonexistent`` to control how the timestamp should be re-localized. + + >>> rng_tz = pd.DatetimeIndex(["2021-10-31 03:30:00"], tz="Europe/Amsterdam") + + >>> rng_tz.floor("2h", ambiguous=False) + DatetimeIndex(['2021-10-31 02:00:00+01:00'], + dtype='datetime64[s, Europe/Amsterdam]', freq=None) + + >>> rng_tz.floor("2h", ambiguous=True) + DatetimeIndex(['2021-10-31 02:00:00+02:00'], + dtype='datetime64[s, Europe/Amsterdam]', freq=None) + """ return self._round(freq, RoundTo.NEAREST_HALF_EVEN, ambiguous, nonexistent) - @Appender((_round_doc + _floor_example).format(op="floor")) def floor( self, freq, ambiguous: TimeAmbiguous = "raise", nonexistent: TimeNonexistent = "raise", ) -> Self: + """ + Perform floor operation on the data to the specified `freq`. + + Parameters + ---------- + freq : str or Offset + The frequency level to floor the index to. Must be a fixed + frequency like 's' (second) not 'ME' (month end). See + :ref:`frequency aliases ` for + a list of possible `freq` values. + ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise' + Only relevant for DatetimeIndex: + + - 'infer' will attempt to infer fall dst-transition hours based on + order + - bool-ndarray where True signifies a DST time, False designates + a non-DST time (note that this flag is only applicable for + ambiguous times) + - 'NaT' will return NaT where there are ambiguous times + - 'raise' will raise a ValueError if there are ambiguous + times. + + nonexistent : 'shift_forward', 'shift_backward', 'NaT', timedelta, + default 'raise' + A nonexistent time does not exist in a particular timezone + where clocks moved forward due to DST. + + - 'shift_forward' will shift the nonexistent time forward to the + closest existing time + - 'shift_backward' will shift the nonexistent time backward to the + closest existing time + - 'NaT' will return NaT where there are nonexistent times + - timedelta objects will shift nonexistent times by the timedelta + - 'raise' will raise a ValueError if there are + nonexistent times. + + Returns + ------- + DatetimeIndex, TimedeltaIndex, or Series + Index of the same type for a DatetimeIndex or TimedeltaIndex, + or a Series with the same index for a Series. + + Raises + ------ + 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, flooring will take place relative to the + local ("wall") time and re-localized to the same timezone. When flooring + near daylight savings time, use ``nonexistent`` and ``ambiguous`` to + control the re-localization behavior. + + Examples + -------- + **DatetimeIndex** + + >>> rng = pd.date_range("1/1/2018 11:59:00", periods=3, freq="min") + >>> rng + DatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00', + '2018-01-01 12:01:00'], + dtype='datetime64[ns]', freq='min') + >>> rng.floor("h") + DatetimeIndex(['2018-01-01 11:00:00', '2018-01-01 12:00:00', + '2018-01-01 12:00:00'], + dtype='datetime64[ns]', freq=None) + + **Series** + + >>> pd.Series(rng).dt.floor("h") + 0 2018-01-01 11:00:00 + 1 2018-01-01 12:00:00 + 2 2018-01-01 12:00:00 + dtype: datetime64[ns] + + When rounding near a daylight savings time transition, use ``ambiguous`` or + ``nonexistent`` to control how the timestamp should be re-localized. + + >>> rng_tz = pd.DatetimeIndex(["2021-10-31 03:30:00"], tz="Europe/Amsterdam") + + >>> rng_tz.floor("2h", ambiguous=False) + DatetimeIndex(['2021-10-31 02:00:00+01:00'], + dtype='datetime64[s, Europe/Amsterdam]', freq=None) + + >>> rng_tz.floor("2h", ambiguous=True) + DatetimeIndex(['2021-10-31 02:00:00+02:00'], + dtype='datetime64[s, Europe/Amsterdam]', freq=None) + """ return self._round(freq, RoundTo.MINUS_INFTY, ambiguous, nonexistent) - @Appender((_round_doc + _ceil_example).format(op="ceil")) def ceil( self, freq, ambiguous: TimeAmbiguous = "raise", nonexistent: TimeNonexistent = "raise", ) -> Self: + """ + Perform ceil operation on the data to the specified `freq`. + + Parameters + ---------- + freq : str or Offset + The frequency level to ceil the index to. Must be a fixed + frequency like 's' (second) not 'ME' (month end). See + :ref:`frequency aliases ` for + a list of possible `freq` values. + ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise' + Only relevant for DatetimeIndex: + + - 'infer' will attempt to infer fall dst-transition hours based on + order + - bool-ndarray where True signifies a DST time, False designates + a non-DST time (note that this flag is only applicable for + ambiguous times) + - 'NaT' will return NaT where there are ambiguous times + - 'raise' will raise a ValueError if there are ambiguous + times. + + nonexistent : 'shift_forward', 'shift_backward', 'NaT', timedelta, + default 'raise' + A nonexistent time does not exist in a particular timezone + where clocks moved forward due to DST. + + - 'shift_forward' will shift the nonexistent time forward to the + closest existing time + - 'shift_backward' will shift the nonexistent time backward to the + closest existing time + - 'NaT' will return NaT where there are nonexistent times + - timedelta objects will shift nonexistent times by the timedelta + - 'raise' will raise a ValueError if there are + nonexistent times. + + Returns + ------- + DatetimeIndex, TimedeltaIndex, or Series + Index of the same type for a DatetimeIndex or TimedeltaIndex, + or a Series with the same index for a Series. + + Raises + ------ + 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, ceiling will take place relative to the + local ("wall") time and re-localized to the same timezone. When ceiling + near daylight savings time, use ``nonexistent`` and ``ambiguous`` to + control the re-localization behavior. + + Examples + -------- + **DatetimeIndex** + + >>> rng = pd.date_range("1/1/2018 11:59:00", periods=3, freq="min") + >>> rng + DatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00', + '2018-01-01 12:01:00'], + dtype='datetime64[ns]', freq='min') + >>> rng.ceil("h") + DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00', + '2018-01-01 13:00:00'], + dtype='datetime64[ns]', freq=None) + + **Series** + + >>> pd.Series(rng).dt.ceil("h") + 0 2018-01-01 12:00:00 + 1 2018-01-01 12:00:00 + 2 2018-01-01 13:00:00 + dtype: datetime64[ns] + + When rounding near a daylight savings time transition, use ``ambiguous`` or + ``nonexistent`` to control how the timestamp should be re-localized. + + >>> rng_tz = pd.DatetimeIndex(["2021-10-31 01:30:00"], tz="Europe/Amsterdam") + + >>> rng_tz.ceil("h", ambiguous=False) + DatetimeIndex(['2021-10-31 02:00:00+01:00'], + dtype='datetime64[s, Europe/Amsterdam]', freq=None) + + >>> rng_tz.ceil("h", ambiguous=True) + DatetimeIndex(['2021-10-31 02:00:00+02:00'], + dtype='datetime64[s, Europe/Amsterdam]', freq=None) + """ return self._round(freq, RoundTo.PLUS_INFTY, ambiguous, nonexistent) # --------------------------------------------------------------