DOC: update the DataFrame.to_hdf() docstirng
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:

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

Write the contained data to an HDF5 file using HDFStore.

Hierarchical Data Format (HDF) is self-describing, allowing an
application to interpret the structure and contents of a file with
no outside information. One HDF file can hold a mix of related objects
which can be accessed as a group or as individual objects.

In order to add another :class:`~pandas.DataFrame` or
:class:`~pandas.Series` to an existing HDF file please use append mode
and different a key.

Parameters
----------
path_or_buf : str or pandas.HDFStore
    File path or HDFStore object.
key : str
    Identifier for the group in the store.
mode : {'a', 'w', 'r+'}, default is 'a'
    Mode to open file:
        - ``'w'``: write, a new file is created (an existing file with
            the same name would be deleted).
        - ``'a'``: append, an existing file is opened for reading and
            writing, and if the file does not exist it is created.
        - `'r+'`: similar to ``'a'``, but the file must already exist.
format : {'fixed', 'table'}, default is 'fixed'
    Possible values:
        - fixed: Fixed format. Fast writing/reading. Not-appendable,
            nor searchable.
        - table: Table format. Write as a PyTables Table structure
            which may perform worse but allow more flexible operations
            like searching / selecting subsets of the data.
append : boolean, default False
    For Table formats, append the input data to the existing.
data_columns :  list of columns or True, optional
    List of columns to create as indexed data columns for on-disk
    queries, or True to use all columns. By default only the axes
    of the object are indexed. See `here
    <http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns>`__.
    Applicable only to format='table'.
complevel : {0-9}, optional
    Specifies a compression level for data.
    A value of 0 disables compression.
complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib'
    Specifies the compression library to be used.
    As of v0.20.2 these additional compressors for Blosc are supported
    (default if no compressor specified: 'blosc:blosclz'):
    {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy',
    'blosc:zlib', 'blosc:zstd'}.
    Specifying a compression library which is not available issues
    a ValueError.
fletcher32 : bool, default False
    If applying compression use the fletcher32 checksum.
dropna : bool, default False
    If true, ALL nan rows will not be written to store.

See Also
--------
DataFrame.to_csv : write out to a csv file.
DataFrame.to_sql : write to a sql table.
DataFrame.to_feather : write out feather-format for DataFrames.

Examples
--------
>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]},
...                   index=['a', 'b', 'c'])
>>> df.to_hdf('data.h5', key='df', mode='w')

We can append another object to the same file:

>>> s = pd.Series([1, 2, 3, 4])
>>> s.to_hdf('data.h5', key='s')

Reading from HDF file:

>>> pd.read_hdf('data.h5', 'df')
A  B
a  1  4
b  2  5
c  3  6
>>> pd.read_hdf('data.h5', 's')
0    1
1    2
2    3
3    4
dtype: int64

Notes
-----
Learn more about `Hierarchical Data Format (HDF)
<https://support.hdfgroup.org/HDF5/whatishdf5.html>`__.

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

Errors found:
	Errors in parameters section
		Parameters {'kwargs'} not documented
		Unknown parameters {'format', 'complevel', 'mode', 'append', 'complib', 'dropna', 'data_columns', 'fletcher32'}
	No returns section found
```

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.

- **kwargs are actually misleading and should be put as args, will do it in separate pull request.
- returns None


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