def backfill(self, limit=None):
return self._upsample('<STR_LIT>', limit=limit)<EOL>
Backward fill the new missing values in the resampled data.

In statistics, imputation is the process of replacing missing data with
substituted values [1]_. When resampling data, missing values may
appear (e.g., when the resampling frequency is higher than the original
frequency). The backward fill will replace NaN values that appeared in
the resampled data with the next value in the original sequence.
Missing values that existed in the original data will not be modified.

Parameters
----------
limit : integer, optional
    Limit of how many values to fill.

Returns
-------
Series, DataFrame
    An upsampled Series or DataFrame with backward filled NaN values.

See Also
--------
bfill : Alias of backfill.
fillna : Fill NaN values using the specified method, which can be
    'backfill'.
nearest : Fill NaN values with nearest neighbor starting from center.
pad : Forward fill NaN values.
Series.fillna : Fill NaN values in the Series using the
    specified method, which can be 'backfill'.
DataFrame.fillna : Fill NaN values in the DataFrame using the
    specified method, which can be 'backfill'.

References
----------
.. [1] https://en.wikipedia.org/wiki/Imputation_(statistics)

Examples
--------

Resampling a Series:

>>> s = pd.Series([1, 2, 3],
...               index=pd.date_range('20180101', periods=3, freq='h'))
>>> s
2018-01-01 00:00:00    1
2018-01-01 01:00:00    2
2018-01-01 02:00:00    3
Freq: H, dtype: int64

>>> s.resample('30min').backfill()
2018-01-01 00:00:00    1
2018-01-01 00:30:00    2
2018-01-01 01:00:00    2
2018-01-01 01:30:00    3
2018-01-01 02:00:00    3
Freq: 30T, dtype: int64

>>> s.resample('15min').backfill(limit=2)
2018-01-01 00:00:00    1.0
2018-01-01 00:15:00    NaN
2018-01-01 00:30:00    2.0
2018-01-01 00:45:00    2.0
2018-01-01 01:00:00    2.0
2018-01-01 01:15:00    NaN
2018-01-01 01:30:00    3.0
2018-01-01 01:45:00    3.0
2018-01-01 02:00:00    3.0
Freq: 15T, dtype: float64

Resampling a DataFrame that has missing values:

>>> df = pd.DataFrame({'a': [2, np.nan, 6], 'b': [1, 3, 5]},
...                   index=pd.date_range('20180101', periods=3,
...                                       freq='h'))
>>> df
                       a  b
2018-01-01 00:00:00  2.0  1
2018-01-01 01:00:00  NaN  3
2018-01-01 02:00:00  6.0  5

>>> df.resample('30min').backfill()
                       a  b
2018-01-01 00:00:00  2.0  1
2018-01-01 00:30:00  NaN  3
2018-01-01 01:00:00  NaN  3
2018-01-01 01:30:00  6.0  5
2018-01-01 02:00:00  6.0  5

>>> df.resample('15min').backfill(limit=2)
                       a    b
2018-01-01 00:00:00  2.0  1.0
2018-01-01 00:15:00  NaN  NaN
2018-01-01 00:30:00  NaN  3.0
2018-01-01 00:45:00  NaN  3.0
2018-01-01 01:00:00  NaN  3.0
2018-01-01 01:15:00  NaN  NaN
2018-01-01 01:30:00  6.0  5.0
2018-01-01 01:45:00  6.0  5.0
2018-01-01 02:00:00  6.0  5.0