I am querying a table from the Databricks Catalog which I have to filter several columns with the same criteria.  below is what I have created so far.  I have 10 columns that I have filter with a set of criteria from (dx_list1) and another 10 that I have to filter with another set of criteria (dx_list2).  

I have started doing this: col("DX_00").isin(dx_list1) | col("DX_01").isin (dx_list2) and was planning to go all the way to DX_19.  

I am wondering if there is a more efficient way to get the same results or is this as good as it gets.  Thank you

Code below:

dx_list1 = ['J984', 'J466', 'J754', 'J64', 'J465', 'J445']

dx_list2 = ['J454','J445','J443','J76','J487','J765','J765']

test = spark.table("claim").select("ID","MEMB_KEY","PROV_KEY","CL#","DOS","DX_00","DX_01","DX_02","DX_03",
"DX_04","DX_05","DX_06","DX_07","DX_08","DX_09","DX_10")

.filter(
col("DX_00").isin(dx_list1) | col("DX_01").isin (dx_list2) &
col("DX_02").isin(dx_list1) | col("DX_03").isin (dx_list2) &
col("DX_04").isin(dx_list1) | col("DX_05").isin (dx_list2)
)

display(test)