snowflake.snowpark.Session.table_function¶

Session.table_function(func_name: str | List[str] | TableFunctionCall, *func_arguments: ColumnOrName, **func_named_arguments: ColumnOrName) → DataFrame[source]¶

Creates a new DataFrame from the given snowflake SQL table function.

References: Snowflake SQL functions.

Example 1

Query a table function by function name:

>>> from snowflake.snowpark.functions import lit
>>> session.table_function("split_to_table", lit("split words to table"), lit(" ")).collect()
[Row(SEQ=1, INDEX=1, VALUE='split'), Row(SEQ=1, INDEX=2, VALUE='words'), Row(SEQ=1, INDEX=3, VALUE='to'), Row(SEQ=1, INDEX=4, VALUE='table')]

Example 2

Define a table function variable and query it:

>>> from snowflake.snowpark.functions import table_function, lit
>>> split_to_table = table_function("split_to_table")
>>> session.table_function(split_to_table(lit("split words to table"), lit(" "))).collect()
[Row(SEQ=1, INDEX=1, VALUE='split'), Row(SEQ=1, INDEX=2, VALUE='words'), Row(SEQ=1, INDEX=3, VALUE='to'), Row(SEQ=1, INDEX=4, VALUE='table')]

Example 3

If you want to call a UDTF right after it’s registered, the returned UserDefinedTableFunction is callable:

>>> from snowflake.snowpark.types import IntegerType, StructField, StructType
>>> from snowflake.snowpark.functions import udtf, lit
>>> class GeneratorUDTF:
...     def process(self, n):
...         for i in range(n):
...             yield (i, )
>>> generator_udtf = udtf(GeneratorUDTF, output_schema=StructType([StructField("number", IntegerType())]), input_types=[IntegerType()])
>>> session.table_function(generator_udtf(lit(3))).collect()
[Row(NUMBER=0), Row(NUMBER=1), Row(NUMBER=2)]

Parameters:

func_name – The SQL function name.
func_arguments – The positional arguments for the SQL function.
func_named_arguments – The named arguments for the SQL function, if it accepts named arguments.

Returns:

A new DataFrame with data from calling the table function.