Pyspark: Pass multiple columns in UDF

Question

I am writing a User Defined Function which will take all the columns except the first one in a dataframe and do sum (or any other operation). Now the dataframe can sometimes

Answer 1

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|
+---+---+---+---+------+