def between_time(self, start_time: Union[(datetime.time, str)], end_time: Union[(datetime.time, str)], include_start: bool=True, include_end: bool=True, axis: Axis=0) -> 'DataFrame':
    "\n        Select values between particular times of the day (example: 9:00-9:30 AM).\n\n        By setting ``start_time`` to be later than ``end_time``,\n        you can get the times that are *not* between the two times.\n\n        Parameters\n        ----------\n        start_time : datetime.time or str\n            Initial time as a time filter limit.\n        end_time : datetime.time or str\n            End time as a time filter limit.\n        include_start : bool, default True\n            Whether the start time needs to be included in the result.\n        include_end : bool, default True\n            Whether the end time needs to be included in the result.\n        axis : {0 or 'index', 1 or 'columns'}, default 0\n            Determine range time on index or columns value.\n\n        Returns\n        -------\n        DataFrame\n            Data from the original object filtered to the specified dates range.\n\n        Raises\n        ------\n        TypeError\n            If the index is not  a :class:`DatetimeIndex`\n\n        See Also\n        --------\n        at_time : Select values at a particular time of the day.\n        first : Select initial periods of time series based on a date offset.\n        last : Select final periods of time series based on a date offset.\n        DatetimeIndex.indexer_between_time : Get just the index locations for\n            values between particular times of the day.\n\n        Examples\n        --------\n        >>> idx = pd.date_range('2018-04-09', periods=4, freq='1D20min')\n        >>> psdf = ps.DataFrame({'A': [1, 2, 3, 4]}, index=idx)\n        >>> psdf\n                             A\n        2018-04-09 00:00:00  1\n        2018-04-10 00:20:00  2\n        2018-04-11 00:40:00  3\n        2018-04-12 01:00:00  4\n\n        >>> psdf.between_time('0:15', '0:45')\n                             A\n        2018-04-10 00:20:00  2\n        2018-04-11 00:40:00  3\n\n        You get the times that are *not* between two times by setting\n        ``start_time`` later than ``end_time``:\n\n        >>> psdf.between_time('0:45', '0:15')\n                             A\n        2018-04-09 00:00:00  1\n        2018-04-12 01:00:00  4\n        "
    axis = validate_axis(axis)
    if (axis != 0):
        raise NotImplementedError('between_time currently only works for axis=0')
    if (not isinstance(self.index, ps.DatetimeIndex)):
        raise TypeError('Index must be DatetimeIndex')
    psdf = self.copy()
    psdf.index.name = verify_temp_column_name(psdf, '__index_name__')
    return_types = ([psdf.index.dtype] + list(psdf.dtypes))

    def pandas_between_time(pdf) -> ps.DataFrame[return_types]:
        return pdf.between_time(start_time, end_time, include_start, include_end).reset_index()
    with option_context('compute.default_index_type', 'distributed'):
        psdf = psdf.pandas_on_spark.apply_batch(pandas_between_time)
    return DataFrame(self._internal.copy(spark_frame=psdf._internal.spark_frame, index_spark_columns=psdf._internal.data_spark_columns[:1], index_fields=psdf._internal.data_fields[:1], data_spark_columns=psdf._internal.data_spark_columns[1:], data_fields=psdf._internal.data_fields[1:]))
Select values between particular times of the day (example: 9:00-9:30 AM).

By setting ``start_time`` to be later than ``end_time``,
you can get the times that are *not* between the two times.

Parameters
----------
start_time : datetime.time or str
    Initial time as a time filter limit.
end_time : datetime.time or str
    End time as a time filter limit.
include_start : bool, default True
    Whether the start time needs to be included in the result.
include_end : bool, default True
    Whether the end time needs to be included in the result.
axis : {0 or 'index', 1 or 'columns'}, default 0
    Determine range time on index or columns value.

Returns
-------
DataFrame
    Data from the original object filtered to the specified dates range.

Raises
------
TypeError
    If the index is not  a :class:`DatetimeIndex`

See Also
--------
at_time : Select values at a particular time of the day.
first : Select initial periods of time series based on a date offset.
last : Select final periods of time series based on a date offset.
DatetimeIndex.indexer_between_time : Get just the index locations for
    values between particular times of the day.

Examples
--------
>>> idx = pd.date_range('2018-04-09', periods=4, freq='1D20min')
>>> psdf = ps.DataFrame({'A': [1, 2, 3, 4]}, index=idx)
>>> psdf
                     A
2018-04-09 00:00:00  1
2018-04-10 00:20:00  2
2018-04-11 00:40:00  3
2018-04-12 01:00:00  4

>>> psdf.between_time('0:15', '0:45')
                     A
2018-04-10 00:20:00  2
2018-04-11 00:40:00  3

You get the times that are *not* between two times by setting
``start_time`` later than ``end_time``:

>>> psdf.between_time('0:45', '0:15')
                     A
2018-04-09 00:00:00  1
2018-04-12 01:00:00  4
def between_time(self, start_time: Union[(datetime.time, str)], end_time: Union[(datetime.time, str)], include_start: bool=True, include_end: bool=True, axis: Axis=0) -> 'DataFrame':
    "\n        Select values between particular times of the day (example: 9:00-9:30 AM).\n\n        By setting ``start_time`` to be later than ``end_time``,\n        you can get the times that are *not* between the two times.\n\n        Parameters\n        ----------\n        start_time : datetime.time or str\n            Initial time as a time filter limit.\n        end_time : datetime.time or str\n            End time as a time filter limit.\n        include_start : bool, default True\n            Whether the start time needs to be included in the result.\n        include_end : bool, default True\n            Whether the end time needs to be included in the result.\n        axis : {0 or 'index', 1 or 'columns'}, default 0\n            Determine range time on index or columns value.\n\n        Returns\n        -------\n        DataFrame\n            Data from the original object filtered to the specified dates range.\n\n        Raises\n        ------\n        TypeError\n            If the index is not  a :class:`DatetimeIndex`\n\n        See Also\n        --------\n        at_time : Select values at a particular time of the day.\n        first : Select initial periods of time series based on a date offset.\n        last : Select final periods of time series based on a date offset.\n        DatetimeIndex.indexer_between_time : Get just the index locations for\n            values between particular times of the day.\n\n        Examples\n        --------\n        >>> idx = pd.date_range('2018-04-09', periods=4, freq='1D20min')\n        >>> psdf = ps.DataFrame({'A': [1, 2, 3, 4]}, index=idx)\n        >>> psdf\n                             A\n        2018-04-09 00:00:00  1\n        2018-04-10 00:20:00  2\n        2018-04-11 00:40:00  3\n        2018-04-12 01:00:00  4\n\n        >>> psdf.between_time('0:15', '0:45')\n                             A\n        2018-04-10 00:20:00  2\n        2018-04-11 00:40:00  3\n\n        You get the times that are *not* between two times by setting\n        ``start_time`` later than ``end_time``:\n\n        >>> psdf.between_time('0:45', '0:15')\n                             A\n        2018-04-09 00:00:00  1\n        2018-04-12 01:00:00  4\n        "
    axis = validate_axis(axis)
    if (axis != 0):
        raise NotImplementedError('between_time currently only works for axis=0')
    if (not isinstance(self.index, ps.DatetimeIndex)):
        raise TypeError('Index must be DatetimeIndex')
    psdf = self.copy()
    psdf.index.name = verify_temp_column_name(psdf, '__index_name__')
    return_types = ([psdf.index.dtype] + list(psdf.dtypes))

    def pandas_between_time(pdf) -> ps.DataFrame[return_types]:
        return pdf.between_time(start_time, end_time, include_start, include_end).reset_index()
    with option_context('compute.default_index_type', 'distributed'):
        psdf = psdf.pandas_on_spark.apply_batch(pandas_between_time)
    return DataFrame(self._internal.copy(spark_frame=psdf._internal.spark_frame, index_spark_columns=psdf._internal.data_spark_columns[:1], index_fields=psdf._internal.data_fields[:1], data_spark_columns=psdf._internal.data_spark_columns[1:], data_fields=psdf._internal.data_fields[1:]))