def to_parquet(self, fname, engine='<STR_LIT>', compression='<STR_LIT>',<EOL>index=None, partition_cols=None, **kwargs):
from pandas.io.parquet import to_parquet<EOL>to_parquet(self, fname, engine,<EOL>compression=compression, index=index,<EOL>partition_cols=partition_cols, **kwargs)<EOL>
Write a DataFrame to the binary parquet format.

.. versionadded:: 0.21.0

This function writes the dataframe as a `parquet file
<https://parquet.apache.org/>`_. You can choose different parquet
backends, and have the option of compression. See
:ref:`the user guide <io.parquet>` for more details.

Parameters
----------
fname : str
    File path or Root Directory path. Will be used as Root Directory
    path while writing a partitioned dataset.

    .. versionchanged:: 0.24.0

engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'
    Parquet library to use. If 'auto', then the option
    ``io.parquet.engine`` is used. The default ``io.parquet.engine``
    behavior is to try 'pyarrow', falling back to 'fastparquet' if
    'pyarrow' is unavailable.
compression : {'snappy', 'gzip', 'brotli', None}, default 'snappy'
    Name of the compression to use. Use ``None`` for no compression.
index : bool, default None
    If ``True``, include the dataframe's index(es) in the file output.
    If ``False``, they will not be written to the file. If ``None``,
    the behavior depends on the chosen engine.

    .. versionadded:: 0.24.0

partition_cols : list, optional, default None
    Column names by which to partition the dataset
    Columns are partitioned in the order they are given

    .. versionadded:: 0.24.0

**kwargs
    Additional arguments passed to the parquet library. See
    :ref:`pandas io <io.parquet>` for more details.

See Also
--------
read_parquet : Read a parquet file.
DataFrame.to_csv : Write a csv file.
DataFrame.to_sql : Write to a sql table.
DataFrame.to_hdf : Write to hdf.

Notes
-----
This function requires either the `fastparquet
<https://pypi.org/project/fastparquet>`_ or `pyarrow
<https://arrow.apache.org/docs/python/>`_ library.

Examples
--------
>>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})
>>> df.to_parquet('df.parquet.gzip',
...               compression='gzip')  # doctest: +SKIP
>>> pd.read_parquet('df.parquet.gzip')  # doctest: +SKIP
   col1  col2
0     1     3
1     2     4