DOC: Fixed errors in pandas.DataFrame.asfreq PR07, RT02, RT03, SA04
- [X] closes https://github.com/pandanistas/pandanistas_sprint_ui2020/issues/19
- [ ] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry

Output of `python scripts/validate_docstrings.py pandas.DataFrame.asfreq`:
```
################################################################################
##################### Docstring (pandas.DataFrame.asfreq)  #####################
################################################################################

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 or str
    Frequency DateOffset 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).

Returns
-------
converted
    Same type as caller.

See Also
--------
reindex : Conform DataFrame to new index with optional filling logic.

Notes
-----
To learn more about the frequency strings, please see `this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/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

################################################################################
################################## Validation ##################################
################################################################################
```