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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def asfreq(self, freq, method=None, how=None, normalize=False, fill_value=None):
 """ 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. 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. 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``. 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``. 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 """

        from pandas.core.resample import asfreq
        return asfreq(self, freq, method=method, how=how, normalize=normalize,
                      fill_value=fill_value)