Pyspark: Pass multiple columns in UDF

Another simple way without Array and Struct.

from pyspark.sql.types import IntegerType
from pyspark.sql.functions import udf

def sum(x, y):
    return x + y

sum_cols = udf(sum, IntegerType())

a=spark.createDataFrame([(101, 1, 16)], ['ID', 'A', 'B'])
a.show()
a.withColumn('Result', sum_cols('A', 'B')).show()

Use struct instead of array

from pyspark.sql.types import IntegerType
from pyspark.sql.functions import udf, struct
sum_cols = udf(lambda x: x[0]+x[1], IntegerType())
a=spark.createDataFrame([(101, 1, 16)], ['ID', 'A', 'B'])
a.show()
a.withColumn('Result', sum_cols(struct('A', 'B'))).show()

If all columns you want to pass to UDF have the same data type you can use array as input parameter, for example:

>>> from pyspark.sql.types import IntegerType
>>> from pyspark.sql.functions import udf, array
>>> sum_cols = udf(lambda arr: sum(arr), IntegerType())
>>> spark.createDataFrame([(101, 1, 16)], ['ID', 'A', 'B']) \
...     .withColumn('Result', sum_cols(array('A', 'B'))).show()
+---+---+---+------+
| ID|  A|  B|Result|
+---+---+---+------+
|101|  1| 16|    17|
+---+---+---+------+

>>> spark.createDataFrame([(101, 1, 16, 8)], ['ID', 'A', 'B', 'C'])\
...     .withColumn('Result', sum_cols(array('A', 'B', 'C'))).show()
+---+---+---+---+------+
| ID|  A|  B|  C|Result|
+---+---+---+---+------+
|101|  1| 16|  8|    25|
+---+---+---+---+------+