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?
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|
#+---------+