PySpark row-wise function composition

I had a similar problem and found the solution in the answer to this stackoverflow question

To pass multiple columns or a whole row to an UDF use a struct:

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

df = sqlContext.createDataFrame([(None, None), (1, None), (None, 2)], ("a", "b"))

count_empty_columns = udf(lambda row: len([x for x in row if x == None]), IntegerType())

new_df = df.withColumn("null_count", count_empty_columns(struct([df[x] for x in df.columns])))

new_df.show()

returns:

+----+----+----------+
|   a|   b|null_count|
+----+----+----------+
|null|null|         2|
|   1|null|         1|
|null|   2|         1|
+----+----+----------+