# pandas.DataFrame.to_parquet

`DataFrame.``to_parquet`(fname, engine='auto', compression='snappy', index=None, partition_cols=None, **kwargs)

  Write a DataFrame to the binary parquet format.

  New in version 0.21.0.

  This function writes the dataframe as a parquet file. You can choose different parquet backends, and have the option of compression. See the user guide for more details.

  Parameters:

  fname : str

  > File path or Root Directory path. Will be used as Root Directory path while writing a partitioned dataset.

  > Changed in version 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.

  > New in version 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

  > New in version 0.24.0.

  **kwargs

  > Additional arguments passed to the parquet library. See pandas io 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 or pyarrow 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

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