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    Accessor object for datetimelike properties of the Series values.

    Examples
    --------
    >>> seconds_series = pd.Series(pd.date_range("2000-01-01", periods=3, freq="s"))
    >>> seconds_series
    0   2000-01-01 00:00:00
    1   2000-01-01 00:00:01
    2   2000-01-01 00:00:02
    dtype: datetime64[ns]
    >>> seconds_series.dt.second
    0    0
    1    1
    2    2
    dtype: int64

    >>> hours_series = pd.Series(pd.date_range("2000-01-01", periods=3, freq="h"))
    >>> hours_series
    0   2000-01-01 00:00:00
    1   2000-01-01 01:00:00
    2   2000-01-01 02:00:00
    dtype: datetime64[ns]
    >>> hours_series.dt.hour
    0    0
    1    1
    2    2
    dtype: int64

    >>> quarters_series = pd.Series(pd.date_range("2000-01-01", periods=3, freq="q"))
    >>> quarters_series
    0   2000-03-31
    1   2000-06-30
    2   2000-09-30
    dtype: datetime64[ns]
    >>> quarters_series.dt.quarter
    0    1
    1    2
    2    3
    dtype: int64

    Returns a Series indexed like the original Series.
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    )�returncCs|j�j�S)a
        Return the data as an array of native Python datetime objects.

        Timezone information is retained if present.

        .. warning::

           Python's datetime uses microsecond resolution, which is lower than
           pandas (nanosecond). The values are truncated.

        Returns
        -------
        numpy.ndarray
            Object dtype array containing native Python datetime objects.

        See Also
        --------
        datetime.datetime : Standard library value for a datetime.

        Examples
        --------
        >>> s = pd.Series(pd.date_range('20180310', periods=2))
        >>> s
        0   2018-03-10
        1   2018-03-11
        dtype: datetime64[ns]

        >>> s.dt.to_pydatetime()
        array([datetime.datetime(2018, 3, 10, 0, 0),
               datetime.datetime(2018, 3, 11, 0, 0)], dtype=object)

        pandas' nanosecond precision is truncated to microseconds.

        >>> s = pd.Series(pd.date_range('20180310', periods=2, freq='ns'))
        >>> s
        0   2018-03-10 00:00:00.000000000
        1   2018-03-10 00:00:00.000000001
        dtype: datetime64[ns]

        >>> s.dt.to_pydatetime()
        array([datetime.datetime(2018, 3, 10, 0, 0),
               datetime.datetime(2018, 3, 10, 0, 0)], dtype=object)
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        Returns a DataFrame with the year, week, and day calculated according to
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        .. versionadded:: 1.1.0

        Returns
        -------
        DataFrame
            with columns year, week and day

        See Also
        --------
        Timestamp.isocalendar
        datetime.date.isocalendar

        Examples
        --------
        >>> ser = pd.to_datetime(pd.Series(["2010-01-01", pd.NaT]))
        >>> ser.dt.isocalendar()
           year  week  day
        0  2009    53     5
        1  <NA>  <NA>  <NA>
        >>> ser.dt.isocalendar().week
        0      53
        1    <NA>
        Name: week, dtype: UInt32
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        The week ordinal of the year.

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        Series.dt.weekofyear and Series.dt.week have been deprecated.
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�TimedeltaPropertiesa)
    Accessor object for datetimelike properties of the Series values.

    Returns a Series indexed like the original Series.
    Raises TypeError if the Series does not contain datetimelike values.

    Examples
    --------
    >>> seconds_series = pd.Series(
    ...     pd.timedelta_range(start="1 second", periods=3, freq="S")
    ... )
    >>> seconds_series
    0   0 days 00:00:01
    1   0 days 00:00:02
    2   0 days 00:00:03
    dtype: timedelta64[ns]
    >>> seconds_series.dt.seconds
    0    1
    1    2
    2    3
    dtype: int64
    )r<cCs|j�j�S)a�
        Return an array of native `datetime.timedelta` objects.

        Python's standard `datetime` library uses a different representation
        timedelta's. This method converts a Series of pandas Timedeltas
        to `datetime.timedelta` format with the same length as the original
        Series.

        Returns
        -------
        numpy.ndarray
            Array of 1D containing data with `datetime.timedelta` type.

        See Also
        --------
        datetime.timedelta

        Examples
        --------
        >>> s = pd.Series(pd.to_timedelta(np.arange(5), unit="d"))
        >>> s
        0   0 days
        1   1 days
        2   2 days
        3   3 days
        4   4 days
        dtype: timedelta64[ns]

        >>> s.dt.to_pytimedelta()
        array([datetime.timedelta(0), datetime.timedelta(days=1),
        datetime.timedelta(days=2), datetime.timedelta(days=3),
        datetime.timedelta(days=4)], dtype=object)
        )r$�to_pytimedelta)rrrr rK7s"z"TimedeltaProperties.to_pytimedeltacCs|j�jj|jj�S)a�
        Return a Dataframe of the components of the Timedeltas.

        Returns
        -------
        DataFrame

        Examples
        --------
        >>> s = pd.Series(pd.to_timedelta(np.arange(5), unit='s'))
        >>> s
        0   0 days 00:00:00
        1   0 days 00:00:01
        2   0 days 00:00:02
        3   0 days 00:00:03
        4   0 days 00:00:04
        dtype: timedelta64[ns]
        >>> s.dt.components
           days  hours  minutes  seconds  milliseconds  microseconds  nanoseconds
        0     0      0        0        0             0             0            0
        1     0      0        0        1             0             0            0
        2     0      0        0        2             0             0            0
        3     0      0        0        3             0             0            0
        4     0      0        0        4             0             0            0
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    Accessor object for datetimelike properties of the Series values.

    Returns a Series indexed like the original Series.
    Raises TypeError if the Series does not contain datetimelike values.

    Examples
    --------
    >>> seconds_series = pd.Series(
    ...     pd.period_range(
    ...         start="2000-01-01 00:00:00", end="2000-01-01 00:00:03", freq="s"
    ...     )
    ... )
    >>> seconds_series
    0    2000-01-01 00:00:00
    1    2000-01-01 00:00:01
    2    2000-01-01 00:00:02
    3    2000-01-01 00:00:03
    dtype: period[S]
    >>> seconds_series.dt.second
    0    0
    1    1
    2    2
    3    3
    dtype: int64

    >>> hours_series = pd.Series(
    ...     pd.period_range(start="2000-01-01 00:00", end="2000-01-01 03:00", freq="h")
    ... )
    >>> hours_series
    0    2000-01-01 00:00
    1    2000-01-01 01:00
    2    2000-01-01 02:00
    3    2000-01-01 03:00
    dtype: period[H]
    >>> hours_series.dt.hour
    0    0
    1    1
    2    2
    3    3
    dtype: int64

    >>> quarters_series = pd.Series(
    ...     pd.period_range(start="2000-01-01", end="2000-12-31", freq="Q-DEC")
    ... )
    >>> quarters_series
    0    2000Q1
    1    2000Q2
    2    2000Q3
    3    2000Q4
    dtype: period[Q-DEC]
    >>> quarters_series.dt.quarter
    0    1
    1    2
    2    3
    3    4
    dtype: int64
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