spark.
to_spark_io
Write the DataFrame out to a Spark data source. DataFrame.spark.to_spark_io() is an alias of DataFrame.to_spark_io().
DataFrame.spark.to_spark_io()
DataFrame.to_spark_io()
Path to the data source.
Specifies the output data source format. Some common ones are:
‘delta’
‘parquet’
‘orc’
‘json’
‘csv’
‘overwrite’. Specifies the behavior of the save operation when data 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.
Names of partitioning columns
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.
All other options passed directly into Spark’s data source.
See also
read_spark_io, DataFrame.to_delta, DataFrame.to_parquet, DataFrame.to_table, DataFrame.to_spark_io, DataFrame.spark.to_spark_io
read_spark_io
DataFrame.to_delta
DataFrame.to_parquet
DataFrame.to_table
DataFrame.to_spark_io
DataFrame.spark.to_spark_io
Examples
>>> 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_spark_io(path='%s/to_spark_io/foo.json' % path, format='json')