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        Series.mean : Return the mean value in a Series.

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        Examples
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        DatetimeIndex(['2001-01-01', '2001-01-02', '2001-01-03'],
                      dtype='datetime64[ns]', freq='D')
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        >>> tdelta_idx
        TimedeltaIndex(['1 days', '2 days', '3 days'],
                        dtype='timedelta64[ns]', freq=None)
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        Return an Index of formatted strings specified by date_format, which
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        of the string format can be found in `python string format
        doc <%(URL)s>`__.

        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
        preferably replaced with their supported equivalents (such as `"%%H:%%M"`,
        `"%%I:%%M:%%S %%p"`).

        Note that `PeriodIndex` support additional directives, detailed in
        `Period.strftime`.

        Parameters
        ----------
        date_format : str
            Date format string (e.g. "%%Y-%%m-%%d").

        Returns
        -------
        ndarray[object]
            NumPy ndarray of formatted strings.

        See Also
        --------
        to_datetime : Convert the given argument to datetime.
        DatetimeIndex.normalize : Return DatetimeIndex with times to midnight.
        DatetimeIndex.round : Round the DatetimeIndex to the specified freq.
        DatetimeIndex.floor : Floor the DatetimeIndex to the specified freq.
        Timestamp.strftime : Format a single Timestamp.
        Period.strftime : Format a single Period.

        Examples
        --------
        >>> rng = pd.date_range(pd.Timestamp("2018-03-10 09:00"),
        ...                     periods=3, freq='s')
        >>> rng.strftime('%%B %%d, %%Y, %%r')
        Index(['March 10, 2018, 09:00:00 AM', 'March 10, 2018, 09:00:01 AM',
               'March 10, 2018, 09:00:02 AM'],
              dtype='object')
        )r�r�Fr�)r�r�rJr�r�)rr�r�s   rr�strftimezDatelikeOps.strftime�s1��b�*�*�{�2�6�6�*�R���}�}�V�%�}�0�0rtN)r�rr&r+)r�r3r4r5r=r>r�rtrrr;r;�s%����
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    Perform {op} operation on the data to the specified `freq`.

    Parameters
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    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 an AmbiguousTimeError 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 an NonExistentTimeError 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.

    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')
    a�>>> 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[ns, Europe/Amsterdam]', freq=None)

    >>> rng_tz.floor("2h", ambiguous=True)
    DatetimeIndex(['2021-10-31 02:00:00+02:00'],
                  dtype='datetime64[ns, Europe/Amsterdam]', freq=None)
    a�>>> 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[ns, Europe/Amsterdam]', freq=None)

    >>> rng_tz.floor("2h", ambiguous=True)
    DatetimeIndex(['2021-10-31 02:00:00+02:00'],
                  dtype='datetime64[ns, Europe/Amsterdam]', freq=None)
    a�>>> 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[ns, Europe/Amsterdam]', freq=None)

    >>> rng_tz.ceil("h", ambiguous=True)
    DatetimeIndex(['2021-10-31 02:00:00+02:00'],
                  dtype='datetime64[ns, Europe/Amsterdam]', freq=None)
    c����eZdZUdZded<dejdf			d&d�Zed��Z	e
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