Remove extra documents

Author: cloudboat

pandas/core/arrays/datetimelike.py

@@ -1830,156 +1830,6 @@ 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 <timeseries.offset_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[us, Europe/Amsterdam]', freq=None)
-
-    >>> rng_tz.floor("2h", ambiguous=True)
-    DatetimeIndex(['2021-10-31 02:00:00+02:00'],
-                  dtype='datetime64[us, 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[us, Europe/Amsterdam]', freq=None)
-
-    >>> rng_tz.floor("2h", ambiguous=True)
-    DatetimeIndex(['2021-10-31 02:00:00+02:00'],
-                  dtype='datetime64[us, 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[us, Europe/Amsterdam]', freq=None)
-
-    >>> rng_tz.ceil("h", ambiguous=True)
-    DatetimeIndex(['2021-10-31 02:00:00+02:00'],
-                  dtype='datetime64[us, Europe/Amsterdam]', freq=None)
-    """
-
-
 class TimelikeOps(DatetimeLikeArrayMixin):
     """
     Common ops for TimedeltaIndex/DatetimeIndex, but not PeriodIndex.