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

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

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

```
################################################################################
####################### Docstring (pandas.DataFrame.any) #######################
################################################################################

Return whether any element is True over requested axis.

Unlike :meth:`DataFrame.all`, this performs an *or* operation. If any of the
values along the specified axis is True, this will return True.

Parameters
----------
axis : int, default 0
    Select the axis which can be 0 for indices and 1 for columns.
skipna : boolean, default True
    Exclude NA/null values. If an entire row/column is NA, the result
    will be NA.
level : int or level name, default None
    If the axis is a MultiIndex (hierarchical), count along a
    particular level, collapsing into a Series.
bool_only : boolean, default None
    Include only boolean columns. If None, will attempt to use everything,
    then use only boolean data. Not implemented for Series.
**kwargs : any, default None
    Additional keywords have no affect but might be accepted for
    compatibility with numpy.

Returns
-------
any : Series or DataFrame (if level specified)

See Also
--------
pandas.DataFrame.all : Return whether all elements are True.

Examples
--------
**Series**

For Series input, the output is a scalar indicating whether any element
is True.

>>> pd.Series([True, False]).any()
True

**DataFrame**

Whether each column contains at least one True element (the default).

>>> pd.DataFrame({
...     "A": [1, 2, 3],
...     "B": [4, 5, 6]
... }).any()
A    True
B    True
dtype: bool

Aggregating over the columns.

>>> pd.DataFrame({
...     "A": [True, False, True],
...     "B": [4, 5, 6]
... }).any(axis='columns')
0    True
1    True
2    True
dtype: bool

>>> pd.DataFrame({
...     "A": [True, False, True],
...     "B": [4, 0, 6]
... }).any(axis='columns')
0    True
1    False
2    True
dtype: bool

`any` for an empty DataFrame is an empty Series.

>>> pd.DataFrame([]).any()
Series([], dtype: bool)

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

Errors found:
	Use only one blank line to separate sections or paragraphs
	Errors in parameters section
		Parameters {'kwargs'} not documented
		Unknown parameters {'**kwargs'}
```

If the validation script still gives errors, but you think there is a good reason
to deviate in this case (and there are certainly such cases), please state this
explicitly.


Checklist for other PRs (remove this part if you are doing a PR for the pandas documentation sprint):

- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry