How to concat two ArrayType(StringType()) columns element-wise in Pyspark? - apache-spark

Question

I have two ArrayType(StringType()) columns in a spark dataframe, and I want to concatenate the two columns element-wise:
input:
+-------------+-------------+
|col1 |col2 |
+-------------+-------------+
|['a','b'] |['c','d'] |
|['a','b','c']|['e','f','g']|
+-------------+-------------+
output:
+-------------+-------------+----------------+
|col1 |col2 |col3 |
+-------------+-------------+----------------+
|['a','b'] |['c','d'] |['ac', 'bd'] |
|['a','b','c']|['e','f','g']|['ae','bf','cg']|
+-------------+----------- -+----------------+
Thanks.

Answer 1

For Spark 2.4+, you can use zip_with function:
zip_with(left, right, func) - Merges the two given arrays,
element-wise, into a single array using function
df.withColumn("col3", expr("zip_with(col1, col2, (x, y) -> concat(x, y))")).show()
#+------+------+--------+
#| col1| col2| col3|
#+------+------+--------+
#|[a, b]|[c, d]|[ac, bd]|
#+------+------+--------+
Another way using transform function like this:
df.withColumn("col3", expr("transform(col1, (x, i) -> concat(x, col2[i]))"))
The transform function takes as parameters the first array column col1, iterates over its elements and applies a lambda function (x, i) -> concat(x, col2[i]) where x the actual element and i its index used to get the corresponding element from array col2.

Answer 2

Here is an alternative answer that can be used for the updated non-original question. Uses array and array_except to demonstrate the use of such methods. The accepted answer is more elegant.
from pyspark.sql.functions import *
from pyspark.sql.types import *
# Arbitrary max number of elements to apply array over, need not broadcast such a small amount of data afaik.
max_entries = 5
# Gen in this case numeric data, etc. 3 rows with 2 arrays of varying length,but per row constant length.
dfA = spark.createDataFrame([ ( list([x,x+1,4, x+100]), 4, list([x+100,x+200,999,x+500]) ) for x in range(3)], ['array1', 'value1', 'array2'] ).withColumn("s",size(col("array1")))
dfB = spark.createDataFrame([ ( list([x,x+1]), 4, list([x+100,x+200]) ) for x in range(5)], ['array1', 'value1', 'array2'] ).withColumn("s",size(col("array1")))
df = dfA.union(dfB)
# concat the array elements which are variable in size up to a max amount.
df2 = df.select(( [concat(col("array1")[i], col("array2")[i]) for i in range(max_entries)]))
df3 = df2.withColumn("res", array(df2.schema.names))
# Get results but strip out null entires from array.
df3.select(array_except(df3.res, array(lit(None)))).show(truncate=False)
Could not get the s value of column to be passed into range.
This returns:
+------------------------------+
|array_except(res, array(NULL))|
+------------------------------+
|[0100, 1200, 4999, 100500] |
|[1101, 2201, 4999, 101501] |
|[2102, 3202, 4999, 102502] |
|[0100, 1200] |
|[1101, 2201] |
|[2102, 3202] |
|[3103, 4203] |
|[4104, 5204] |
+------------------------------+

Answer 3

It wouldn't really scale, but you could get the 0th and 1st entries in each array and then say col3 is a[0] + b[0] and then a[1] + b[1].
Make all 4 entries separate values and then output them combined.

Answer 4

Here is a generic answer. Just look at res for the result. 2 equally sized arrays, thus n elements for both.
from pyspark.sql.functions import *
from pyspark.sql.types import *
# Gen in this case numeric data, etc. 3 rows with 2 arrays of varying length, but both the same length as in your example
df = spark.createDataFrame([ ( list([x,x+1,4, x+100]), 4, list([x+100,x+200,999,x+500]) ) for x in range(3)], ['array1', 'value1', 'array2'] )
num_array_elements = len(df.select("array1").first()[0])
# concat
df2 = df.select(([ concat(col("array1")[i], col("array2")[i]) for i in range(num_array_elements)]))
df2.withColumn("res", array(df2.schema.names)).show(truncate=False)
returns: