DataFrame.to_orc(path: str, mode: str = 'w', partition_cols: Union[str, List[str], None] = None, index_col: Union[str, List[str], None] = None, **options: OptionalPrimitiveType) → None

Write the DataFrame out as a ORC file or directory.

pathstr, required

Path to write to.


Python write mode, default ‘w’.


mode can accept the strings for Spark writing mode. Such as ‘append’, ‘overwrite’, ‘ignore’, ‘error’, ‘errorifexists’.

  • ‘append’ (equivalent to ‘a’): Append the new data to existing data.

  • ‘overwrite’ (equivalent to ‘w’): Overwrite existing data.

  • ‘ignore’: Silently ignore this operation if data already exists.

  • ‘error’ or ‘errorifexists’: Throw an exception if data already exists.

partition_colsstr or list of str, optional, default None

Names of partitioning columns

index_col: str or list of str, optional, default: None

Column names to be used in Spark to represent pandas-on-Spark’s index. The index name in pandas-on-Spark is ignored. By default, the index is always lost.


All other options passed directly into Spark’s data source.


>>> df = ps.DataFrame(dict(
...    date=list(pd.date_range('2012-1-1 12:00:00', periods=3, freq='M')),
...    country=['KR', 'US', 'JP'],
...    code=[1, 2 ,3]), columns=['date', 'country', 'code'])
>>> df
                 date country  code
0 2012-01-31 12:00:00      KR     1
1 2012-02-29 12:00:00      US     2
2 2012-03-31 12:00:00      JP     3
>>> df.to_orc('%s/to_orc/foo.orc' % path, partition_cols='date')
>>> df.to_orc(
...     '%s/to_orc/foo.orc' % path,
...     mode = 'overwrite',
...     partition_cols=['date', 'country'])