def asfreq(self, freq, method=None, how=None, normalize=False, fill_value=None):
    "\n        Convert TimeSeries to specified frequency.\n\n        Optionally provide filling method to pad/backfill missing values.\n\n        Returns the original data conformed to a new index with the specified\n        frequency. ``resample`` is more appropriate if an operation, such as\n        summarization, is necessary to represent the data at the new frequency.\n\n        Parameters\n        ----------\n        freq : DateOffset object, or string\n        method : {'backfill'/'bfill', 'pad'/'ffill'}, default None\n            Method to use for filling holes in reindexed Series (note this\n            does not fill NaNs that already were present):\n\n            * 'pad' / 'ffill': propagate last valid observation forward to next\n              valid\n            * 'backfill' / 'bfill': use NEXT valid observation to fill\n        how : {'start', 'end'}, default end\n            For PeriodIndex only, see PeriodIndex.asfreq\n        normalize : bool, default False\n            Whether to reset output index to midnight\n        fill_value : scalar, optional\n            Value to use for missing values, applied during upsampling (note\n            this does not fill NaNs that already were present).\n\n            .. versionadded:: 0.20.0\n\n        Returns\n        -------\n        converted : same type as caller\n\n        See Also\n        --------\n        reindex\n\n        Notes\n        -----\n        To learn more about the frequency strings, please see `this link\n        <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__.\n\n        Examples\n        --------\n\n        Start by creating a series with 4 one minute timestamps.\n\n        >>> index = pd.date_range('1/1/2000', periods=4, freq='T')\n        >>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)\n        >>> df = pd.DataFrame({'s':series})\n        >>> df\n                               s\n        2000-01-01 00:00:00    0.0\n        2000-01-01 00:01:00    NaN\n        2000-01-01 00:02:00    2.0\n        2000-01-01 00:03:00    3.0\n\n        Upsample the series into 30 second bins.\n\n        >>> df.asfreq(freq='30S')\n                               s\n        2000-01-01 00:00:00    0.0\n        2000-01-01 00:00:30    NaN\n        2000-01-01 00:01:00    NaN\n        2000-01-01 00:01:30    NaN\n        2000-01-01 00:02:00    2.0\n        2000-01-01 00:02:30    NaN\n        2000-01-01 00:03:00    3.0\n\n        Upsample again, providing a ``fill value``.\n\n        >>> df.asfreq(freq='30S', fill_value=9.0)\n                               s\n        2000-01-01 00:00:00    0.0\n        2000-01-01 00:00:30    9.0\n        2000-01-01 00:01:00    NaN\n        2000-01-01 00:01:30    9.0\n        2000-01-01 00:02:00    2.0\n        2000-01-01 00:02:30    9.0\n        2000-01-01 00:03:00    3.0\n\n        Upsample again, providing a ``method``.\n\n        >>> df.asfreq(freq='30S', method='bfill')\n                               s\n        2000-01-01 00:00:00    0.0\n        2000-01-01 00:00:30    NaN\n        2000-01-01 00:01:00    NaN\n        2000-01-01 00:01:30    2.0\n        2000-01-01 00:02:00    2.0\n        2000-01-01 00:02:30    3.0\n        2000-01-01 00:03:00    3.0\n        "
    from pandas.core.resample import asfreq
    return asfreq(self, freq, method=method, how=how, normalize=normalize, fill_value=fill_value)
Convert TimeSeries to specified frequency.

Optionally provide filling method to pad/backfill missing values.

Returns the original data conformed to a new index with the specified
frequency. ``resample`` is more appropriate if an operation, such as
summarization, is necessary to represent the data at the new frequency.

Parameters
----------
freq : DateOffset object, or string
method : {'backfill'/'bfill', 'pad'/'ffill'}, default None
    Method to use for filling holes in reindexed Series (note this
    does not fill NaNs that already were present):

    * 'pad' / 'ffill': propagate last valid observation forward to next
      valid
    * 'backfill' / 'bfill': use NEXT valid observation to fill
how : {'start', 'end'}, default end
    For PeriodIndex only, see PeriodIndex.asfreq
normalize : bool, default False
    Whether to reset output index to midnight
fill_value : scalar, optional
    Value to use for missing values, applied during upsampling (note
    this does not fill NaNs that already were present).

    .. versionadded:: 0.20.0

Returns
-------
converted : same type as caller

See Also
--------
reindex

Notes
-----
To learn more about the frequency strings, please see `this link
<http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__.

Examples
--------

Start by creating a series with 4 one minute timestamps.

>>> index = pd.date_range('1/1/2000', periods=4, freq='T')
>>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
>>> df = pd.DataFrame({'s':series})
>>> df
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:01:00    NaN
2000-01-01 00:02:00    2.0
2000-01-01 00:03:00    3.0

Upsample the series into 30 second bins.

>>> df.asfreq(freq='30S')
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    NaN
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    NaN
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    NaN
2000-01-01 00:03:00    3.0

Upsample again, providing a ``fill value``.

>>> df.asfreq(freq='30S', fill_value=9.0)
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    9.0
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    9.0
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    9.0
2000-01-01 00:03:00    3.0

Upsample again, providing a ``method``.

>>> df.asfreq(freq='30S', method='bfill')
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    NaN
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    2.0
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    3.0
2000-01-01 00:03:00    3.0
def asfreq(self, freq, method=None, how=None, normalize=False, fill_value=None):
    "\n        Convert TimeSeries to specified frequency.\n\n        Optionally provide filling method to pad/backfill missing values.\n\n        Returns the original data conformed to a new index with the specified\n        frequency. ``resample`` is more appropriate if an operation, such as\n        summarization, is necessary to represent the data at the new frequency.\n\n        Parameters\n        ----------\n        freq : DateOffset object, or string\n        method : {'backfill'/'bfill', 'pad'/'ffill'}, default None\n            Method to use for filling holes in reindexed Series (note this\n            does not fill NaNs that already were present):\n\n            * 'pad' / 'ffill': propagate last valid observation forward to next\n              valid\n            * 'backfill' / 'bfill': use NEXT valid observation to fill\n        how : {'start', 'end'}, default end\n            For PeriodIndex only, see PeriodIndex.asfreq\n        normalize : bool, default False\n            Whether to reset output index to midnight\n        fill_value : scalar, optional\n            Value to use for missing values, applied during upsampling (note\n            this does not fill NaNs that already were present).\n\n            .. versionadded:: 0.20.0\n\n        Returns\n        -------\n        converted : same type as caller\n\n        See Also\n        --------\n        reindex\n\n        Notes\n        -----\n        To learn more about the frequency strings, please see `this link\n        <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__.\n\n        Examples\n        --------\n\n        Start by creating a series with 4 one minute timestamps.\n\n        >>> index = pd.date_range('1/1/2000', periods=4, freq='T')\n        >>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)\n        >>> df = pd.DataFrame({'s':series})\n        >>> df\n                               s\n        2000-01-01 00:00:00    0.0\n        2000-01-01 00:01:00    NaN\n        2000-01-01 00:02:00    2.0\n        2000-01-01 00:03:00    3.0\n\n        Upsample the series into 30 second bins.\n\n        >>> df.asfreq(freq='30S')\n                               s\n        2000-01-01 00:00:00    0.0\n        2000-01-01 00:00:30    NaN\n        2000-01-01 00:01:00    NaN\n        2000-01-01 00:01:30    NaN\n        2000-01-01 00:02:00    2.0\n        2000-01-01 00:02:30    NaN\n        2000-01-01 00:03:00    3.0\n\n        Upsample again, providing a ``fill value``.\n\n        >>> df.asfreq(freq='30S', fill_value=9.0)\n                               s\n        2000-01-01 00:00:00    0.0\n        2000-01-01 00:00:30    9.0\n        2000-01-01 00:01:00    NaN\n        2000-01-01 00:01:30    9.0\n        2000-01-01 00:02:00    2.0\n        2000-01-01 00:02:30    9.0\n        2000-01-01 00:03:00    3.0\n\n        Upsample again, providing a ``method``.\n\n        >>> df.asfreq(freq='30S', method='bfill')\n                               s\n        2000-01-01 00:00:00    0.0\n        2000-01-01 00:00:30    NaN\n        2000-01-01 00:01:00    NaN\n        2000-01-01 00:01:30    2.0\n        2000-01-01 00:02:00    2.0\n        2000-01-01 00:02:30    3.0\n        2000-01-01 00:03:00    3.0\n        "
    from pandas.core.resample import asfreq
    return asfreq(self, freq, method=method, how=how, normalize=normalize, fill_value=fill_value)