DOC: update the pandas.DataFrame.clip docstring
Checklist for the pandas documentation sprint (ignore this if you are doing
an unrelated PR):

- [x] PR title is "DOC: update the <your-function-or-method> docstring"
- [x] The validation script passes: `scripts/validate_docstrings.py <your-function-or-method>`
- [x] The PEP8 style check passes: `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] The html version looks good: `python doc/make.py --single <your-function-or-method>`
- [x] It has been proofread on language by another sprint participant

Please include the output of the validation script below between the "```" ticks:

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

Errors found:
	Errors in parameters section
		Parameters {'kwargs', 'args'} not documented
		Unknown parameters {'*args, **kwargs'}
		Parameter "*args, **kwargs" has no type
(pandas_dev) Demetris-Retina-MacBook-Pro:pandas demetri$ git diff upstream/master -u -- "*.py" | flake8 --diff
(pandas_dev) Demetris-Retina-MacBook-Pro:pandas demetri$ python scripts/validate_docstrings.py pandas.DataFrame.clip

################################################################################
###################### Docstring (pandas.DataFrame.clip)  ######################
################################################################################

Trim values at input threshold(s).

Elements above/below the upper/lower thresholds will be changed to
upper/lower thresholds. Clipping data is a method for dealing with
out-of-range elements. If some elements are too large or too small,
clipping is one way to transform the data into a reasonable range.

Parameters
----------
lower : float, array-like or None, default None
    Lower threshold for clipping.  Values smaller than `lower` will be
    converted to `lower`.
upper : float, array-like or None, default None
    Upper threshold for clipping.  Values larger than `upper` will be
    converted to `upper`.
axis : {0 or 'index', 1 or 'columns', None}, default None
    Apply clip by index (i.e. by rows) or columns.
inplace : boolean, default False
    Whether to perform the operation in place on the data
        .. versionadded:: 0.21.0.
*args, **kwargs
    Additional keywords have no effect but might be accepted
    for compatibility with numpy.

Returns
-------
`Series` or `DataFrame`.
    Original input with those values above/below the
    `upper`/`lower` thresholds set to the threshold values.

References
-----
.. [1] Tukey, John W. "The future of data analysis." The annals of
    mathematical statistics 33.1 (1962): 1-67.

See Also
--------
DataFrame.clip : Trim values at input threshold(s).
Series.clip : Trim values at input threshold(s).
Series.clip_lower : Return copy of the input with values below given
    value(s) truncated.
Series.clip_upper : Return copy of input with values above given
    value(s) truncated.
DataFrame.clip_lower : Return copy of the input with values below given
    value(s) truncated.
DataFrame.clip_upper : Return copy of input with values above given
    value(s) truncated.
DataFrame.quantile : Return values at the given quantile over requested
    axis, a la numpy.percentile.

Examples
--------
>>> df = pd.DataFrame({'a': [-1, -2, -100],
...                    'b': [1, 2, 100]},
...                    index=['foo', 'bar', 'foobar'])
>>> df
     a  b
foo -1  1
bar -2  2
foobar  -100    100

>>> df.clip(lower=-10, upper=10)
     a  b
foo -1  1
bar -2  2
foobar  -10 10

You can clip each column or row with different thresholds by passing
a ``Series`` to the lower/upper argument. Use the axis argument to clip
by column or rows.

>>> col_thresh = pd.Series({'a': -5, 'b': 5})
>>> df.clip(lower=col_thresh, axis='columns')
     a  b
foo -1  5
bar -2  5
foobar  -5  100

Clip the foo, bar, and foobar rows with lower thresholds 5, 7, and 10.

>>> row_thresh = pd.Series({'foo': 0, 'bar': 1, 'foobar': 10})
>>> df.clip(lower=row_thresh, axis='index')
    a   b
foo 0   1
bar 1   2
foobar  10  100

Winsorizing [1]_ is a related method, whereby the data are clipped at
the 5th and 95th percentiles. The ``DataFrame.quantile`` method returns
a ``Series`` with column names as index and the quantiles as values.
Use ``axis='columns'`` to apply clipping to columns.

>>> lower, upper = df.quantile(0.05), df.quantile(0.95)
>>> df.clip(lower=lower, upper=upper, axis='columns')
     a  b
foo -1.1    1.1
bar -2.0    2.0
foobar  -90.2   90.2

################################################################################
################################## Validation ##################################
################################################################################

Errors found:
	Errors in parameters section
		Parameters {'kwargs', 'args'} not documented
		Unknown parameters {'*args, **kwargs'}
		Parameter "*args, **kwargs" has no type
```



Changes:

* `*args` and `**kwargs` description taken from #20256 in the name of consistency