DOC: update the pandas.core.resample.Resampler.fillna docstring
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Please include the output of the validation script below between the "```" ticks:

```

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############## Docstring (pandas.core.resample.Resampler.fillna)  ##############
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Fill the new missing values in the resampled data using different
methods.

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 ('bfill') will replace NaN values that appeared in
the resampled data with the next value in the original sequence. The
forward fill ('ffill'), on the other hand, will replace NaN values
that appeared in the resampled data with the previous value in the
original sequence. Missing values that existed in the orginal data will
not be modified.

Parameters
----------
method : str, method of resampling ('ffill', 'bfill')
    Method to use for filling holes in resampled data
        * ffill: use previous valid observation to fill gap (forward
          fill).
        * bfill: use next valid observation to fill gap (backward
          fill).
limit : integer, optional
    Limit of how many values to fill.

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

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').fillna("bfill")
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').fillna("bfill", 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

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

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').fillna("bfill")
                       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').fillna("bfill", 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

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

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

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

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################################## Validation ##################################
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Docstring for "pandas.core.resample.Resampler.fillna" correct. :)
```