parameters: (
      df: DataFrame,
      path,
      engine: str = "auto",
      compression="snappy",
      index: Optional[bool] = None,
      partition_cols=None,
      **kwargs,
  )
return_statement: return impl.write(
          df,
          path,
          compression=compression,
          index=index,
          partition_cols=partition_cols,
          **kwargs,
      )
docstring: Write a DataFrame to the parquet format.
  
      Parameters
      ----------
      df : DataFrame
      path : 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``, 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.
  
          .. versionadded:: 0.24.0
  
      partition_cols : str or 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 keyword arguments passed to the engine
docstring_summary: Write a DataFrame to the parquet format.
docstring_tokens: Write a DataFrame to the parquet format .
function: def to_parquet(
      df: DataFrame,
      path,
      engine: str = "auto",
      compression="snappy",
      index: Optional[bool] = None,
      partition_cols=None,
      **kwargs,
  ):
      """
      Write a DataFrame to the parquet format.
  
      Parameters
      ----------
      df : DataFrame
      path : 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``, 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.
  
          .. versionadded:: 0.24.0
  
      partition_cols : str or 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 keyword arguments passed to the engine
      """
      if isinstance(partition_cols, str):
          partition_cols = [partition_cols]
      impl = get_engine(engine)
      return impl.write(
          df,
          path,
          compression=compression,
          index=index,
          partition_cols=partition_cols,
          **kwargs,
      )
function_tokens: def to_parquet ( df : DataFrame , path , engine : str = "auto" , compression = "snappy" , index : Optional [ bool ] = None , partition_cols = None , * * kwargs , ) : if isinstance ( partition_cols , str ) : partition_cols = [ partition_cols ] impl = get_engine ( engine ) return impl . write ( df , path , compression = compression , index = index , partition_cols = partition_cols , * * kwargs , )