pandas.DataFrame.to_parquet

DataFrame.to_parquet(self, fname, engine='auto', compression='snappy', index=None, partition_cols=None, **kwargs) [source]

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