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# pandas.DataFrame.to_parquet

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

  Write a DataFrame to the binary parquet format.

  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:
    pathstr, path object, file-like object, or None, default None

      String, path object (implementing `os.PathLike[str]`), or file-like object implementing a binary `write()` function. If None, the result is returned as bytes. If a string or path, it will be used as Root Directory path when writing a partitioned dataset.

    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.

    compressionstr or None, default ‘snappy’

      Name of the compression to use. Use `None` for no compression. Supported options: ‘snappy’, ‘gzip’, ‘brotli’, ‘lz4’, ‘zstd’.

    indexbool, 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`, similar to `True` the dataframe’s index(es) will be saved. However, instead of being saved as values, the RangeIndex will be stored as a range in the metadata so it doesn’t require much space and is faster. Other indexes will be included as columns in the file output.

    partition_colslist, optional, default None

      Column names by which to partition the dataset. Columns are partitioned in the order they are given. Must be None if path is not a string.

    storage_optionsdict, optional

      Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to `urllib.request.Request` as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to `fsspec.open`. Please see `fsspec` and `urllib` for more details, and for more examples on storage options refer here.

    **kwargs

      Additional arguments passed to the parquet library. See pandas io for more details.

  Returns:
    bytes if no path argument is provided else None

  See also

  `read_parquet`

    Read a parquet file.

  `DataFrame.to_orc`

    Write an orc 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')
      >>> pd.read_parquet('df.parquet.gzip')
         col1  col2
      0     1     3
      1     2     4

  If you want to get a buffer to the parquet content you can use a io.BytesIO object, as long as you don’t use partition_cols, which creates multiple files.

      >>> import io
      >>> f = io.BytesIO()
      >>> df.to_parquet(f)
      >>> f.seek(0)
      0
      >>> content = f.read()