Pyspark Array Column - Replace Empty Elements with Default Value - arrays

Question

I have a dataframe with a column which is an array of strings. Some of the elements of the array may be missing like so:
-------------|-------------------------------
ID |array_list
---------------------------------------------
38292786 |[AAA,, JLT] |
38292787 |[DFG] |
38292788 |[SHJ, QKJ, AAA, YTR, CBM] |
38292789 |[DUY, ANK, QJK, POI, CNM, ADD] |
38292790 |[] |
38292791 |[] |
38292792 |[,,, HKJ] |
I would like to replace the missing elements with a default value of "ZZZ". Is there a way to do that? I tried the following code, which is using a transform function and a regular expression:
import pyspark.sql.functions as F
from pyspark.sql.dataframe import DataFrame
def transform(self, f):
return f(self)
DataFrame.transform = transform
df = df.withColumn("array_list2", F.expr("transform(array_list, x -> regexp_replace(x, '', 'ZZZ'))"))
This doesn't give an error but it is producing nonsense. I'm thinking I just don't know the correct way to identify the missing elements of the array - can anyone help me out?
In production our data has around 10 million rows and I am trying to avoid using explode or a UDF (not sure if it's possible to avoid using both though, just need the code run as efficiently as possible). I'm using Spark 2.4.4
This is what I would like my output to look like:
-------------|-------------------------------|-------------------------------
ID |array_list | array_list2
---------------------------------------------|-------------------------------
38292786 |[AAA,, JLT] |[AAA, ZZZ, JLT]
38292787 |[DFG] |[DFG]
38292788 |[SHJ, QKJ, AAA, YTR, CBM] |[SHJ, QKJ, AAA, YTR, CBM]
38292789 |[DUY, ANK, QJK, POI, CNM, ADD] |[DUY, ANK, QJK, POI, CNM, ADD]
38292790 |[] |[ZZZ]
38292791 |[] |[ZZZ]
38292792 |[,,, HKJ] |[ZZZ, ZZZ, ZZZ, HKJ]

Answer 1

The regex_replace works at character level.
I could not get it to work with transform either, but with help from the first answerer I used a UDF - not that easy.
Here is my example with my data, you can tailor.
%python
from pyspark.sql.types import StringType, ArrayType
from pyspark.sql.functions import udf, col
concat_udf = udf(
lambda con_str, arr: [
x if x is not None else con_str for x in arr or [None]
],
ArrayType(StringType()),
)
arrayData = [
('James',['Java','Scala']),
('Michael',['Spark','Java',None]),
('Robert',['CSharp','']),
('Washington',None),
('Jefferson',['1','2'])]
df = spark.createDataFrame(data=arrayData, schema = ['name','knownLanguages'])
df = df.withColumn("knownLanguages", concat_udf(lit("ZZZ"), col("knownLanguages")))
df.show()
returns:
+----------+------------------+
| name| knownLanguages|
+----------+------------------+
| James| [Java, Scala]|
| Michael|[Spark, Java, ZZZ]|
| Robert| [CSharp, ]|
|Washington| [ZZZ]|
| Jefferson| [1, 2]|
+----------+------------------+
Quite tough this, had some help from the first answerer.

Answer 2

I'm thinking of something, but i'm not sure if it is efficient.
from pyspark.sql import functions as F
df.withColumn("array_list2", F.split(F.array_join("array_list", ",", "ZZZ"), ","))
First I concatenate the values as a string with a delimiter , (hoping you don't have it in your string but you can use something else). I use the null_replacement option to fill the null values. Then I split according to the same delimiter.
EDIT: Based on #thebluephantom comment, you can try this solution :
df.withColumn(
"array_list_2", F.expr(" transform(array_list, x -> coalesce(x, 'ZZZ'))")
).show()
SQL built-in transform is not working for me, so I couldn't try it but hopefully you'll have the result you wanted.