[SOLVED] Alteryx regex_countmatches equivalent in PySpark?

Alteryx regex_countmatches equivalent in PySpark?

I am working on some alteryx workflow migration to PySpark task, as part of which came across the following filter condition.

length([acc_id]) = 9
AND 
(REGEX_CountMatches(right([acc_id],7),"[[:alpha:]]")=0 AND 
REGEX_CountMatches(left([acc_id],2),"[[:alpha:]]")=2)
OR
(REGEX_CountMatches(right([acc_id],7),"[[:alpha:]]")=0 AND 
REGEX_CountMatches(left([acc_id],1),"[[:alpha:]]")=1 AND 
REGEX_CountMatches(right(left([acc_id],2),1), '9')=1 
)

Can someone help me in re-writing this condition in PySpark dataframe?

Solution

You can use length with regexp_replace to get the equivalent of Alteryx's REGEX_CountMatches function :

REGEX_CountMatches(right([acc_id],7),"[[:alpha:]]")=0

Becomes:

# replace all non aplhapetic caracters with '' then get length
F.length(F.regexp_replace(F.expr("right(acc_id, 7)"), '[^A-Za-z]', '')) == 0

right and left functions are only available in SQL, you can use them with expr.

Full example:

from pyspark.sql import functions as F


df = spark.createDataFrame([("AB1234567",), ("AD234XG1234TT5",)], ["acc_id"])

def regex_count_matches(c: Column, regex: str) -> Column:
    """
    helper function equivalent to REGEX_CountMatches
    """
    return F.length(F.regexp_replace(c, regex, ''))


df.filter(
    (F.length("acc_id") == 9) &
    (
      (regex_count_matches(F.expr("right(acc_id, 7)"), '[^A-Za-z]') == 0)
      & (regex_count_matches(F.expr("left(acc_id, 2)"), '[^A-Za-z]') == 2)
    ) | (
      (regex_count_matches(F.expr("right(acc_id, 7)"), '[^A-Za-z]') == 0)
      & (regex_count_matches(F.expr("left(acc_id, 1)"), '[^A-Za-z]') == 1)
      & (regex_count_matches(F.expr("right(left(acc_id, 2), 1)"), '[^9]') == 1)
    )
).show()

#+---------+
#|   acc_id|
#+---------+
#|AB1234567|
#+---------+