How to zip two array columns in Spark SQL

A Spark SQL equivalent of Python's would be pyspark.sql.functions.arrays_zip:

pyspark.sql.functions.arrays_zip(*cols)

Collection function: Returns a merged array of structs in which the N-th struct contains all N-th values of input arrays.

So if you already have two arrays:

from pyspark.sql.functions import split

df = (spark
    .createDataFrame([('abc, def, ghi', '1.0, 2.0, 3.0')])
    .toDF("column_1", "column_2")
    .withColumn("column_1", split("column_1", "\s*,\s*"))
    .withColumn("column_2", split("column_2", "\s*,\s*")))

You can just apply it on the result

from pyspark.sql.functions import arrays_zip

df_zipped = df.withColumn(
  "zipped", arrays_zip("column_1", "column_2")
)

df_zipped.select("zipped").show(truncate=False)

+------------------------------------+
|zipped                              |
+------------------------------------+
|[[abc, 1.0], [def, 2.0], [ghi, 3.0]]|
+------------------------------------+

Now to combine the results you can transform (How to use transform higher-order function?, TypeError: Column is not iterable - How to iterate over ArrayType()?):

df_zipped_concat = df_zipped.withColumn(
    "zipped_concat",
     expr("transform(zipped, x -> concat_ws('_', x.column_1, x.column_2))")
) 

df_zipped_concat.select("zipped_concat").show(truncate=False)

+---------------------------+
|zipped_concat              |
+---------------------------+
|[abc_1.0, def_2.0, ghi_3.0]|
+---------------------------+

Note:

Higher order functions transform and arrays_zip has been introduced in Apache Spark 2.4.

You can also UDF to zip the split array columns,

df = spark.createDataFrame([('abc,def,ghi','1.0,2.0,3.0')], ['col1','col2']) 
+-----------+-----------+
|col1       |col2       |
+-----------+-----------+
|abc,def,ghi|1.0,2.0,3.0|
+-----------+-----------+ ## Hope this is how your dataframe is

from pyspark.sql import functions as F
from pyspark.sql.types import *

def concat_udf(*args):
    return ['_'.join(x) for x in zip(*args)]

udf1 = F.udf(concat_udf,ArrayType(StringType()))
df = df.withColumn('col3',udf1(F.split(df.col1,','),F.split(df.col2,',')))
df.show(1,False)
+-----------+-----------+---------------------------+
|col1       |col2       |col3                       |
+-----------+-----------+---------------------------+
|abc,def,ghi|1.0,2.0,3.0|[abc_1.0, def_2.0, ghi_3.0]|
+-----------+-----------+---------------------------+