diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index a66d00fff9714..8151d6ca3b193 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -1697,10 +1697,15 @@ def to_parquet(self, fname, engine='auto', compression='snappy',
 
         .. 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
-            string file path
+            String file path.
         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``
@@ -1708,8 +1713,31 @@ def to_parquet(self, fname, engine='auto', compression='snappy',
             'pyarrow' is unavailable.
         compression : {'snappy', 'gzip', 'brotli', None}, default 'snappy'
             Name of the compression to use. Use ``None`` for no compression.
-        kwargs
-            Additional keyword arguments passed to the engine
+        **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.python.org/pypi/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')
+        >>> pd.read_parquet('df.parquet.gzip')
+           col1  col2
+        0     1     3
+        1     2     4
         """
         from pandas.io.parquet import to_parquet
         to_parquet(self, fname, engine,