spark.
to_table
Write the DataFrame into a Spark table. DataFrame.spark.to_table() is an alias of DataFrame.to_table().
DataFrame.spark.to_table()
DataFrame.to_table()
Table name in Spark.
Specifies the output data source format. Some common ones are:
‘delta’
‘parquet’
‘orc’
‘json’
‘csv’
‘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.
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.
Additional options passed directly to Spark.
See also
read_table, DataFrame.to_spark_io, DataFrame.spark.to_spark_io, DataFrame.to_parquet
read_table
DataFrame.to_spark_io
DataFrame.spark.to_spark_io
DataFrame.to_parquet
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_table('%s.my_table' % db, partition_cols='date')