DataFrame.to_table(name: str, format: Optional[str] = None, mode: str = 'overwrite', partition_cols: Union[str, List[str], None] = None, index_col: Union[str, List[str], None] = None, **options) → None[source]

Write the DataFrame into a Spark table. DataFrame.spark.to_table() is an alias of DataFrame.to_table().

namestr, required

Table name in Spark.

formatstring, optional

Specifies the output data source format. Some common ones are:

  • ‘delta’

  • ‘parquet’

  • ‘orc’

  • ‘json’

  • ‘csv’

modestr {‘append’, ‘overwrite’, ‘ignore’, ‘error’, ‘errorifexists’}, default

‘overwrite’. Specifies the behavior of the save operation when the table exists already.

  • ‘append’: Append the new data to existing data.

  • ‘overwrite’: 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 Koalas’ index. The index name in Koalas is ignored. By default, the index is always lost.


Additional options passed directly to Spark.



>>> df = ks.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_table('%s.my_table' % db, partition_cols='date')