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 defaultio.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. IfFalse
, they will not be written to the file. IfNone
, 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