How to convert a Spark Dataframe column from vector to a set?

Question

I need to process a dataset to identify frequent itemsets. So the input column must be a vector. The original column is a string with the items separated by comma, so i did the following:

functions.split(out_1['skills'], ',')

The problem is the, for some rows, I have duplicated values in the skills and this is causing an error when trying to identify the frequent itemsets.

I wanted to convert the vector to a set to remove the duplicated elements. Something like this:

functions.to_set(functions.split(out_1['skills'], ','))

But I could not find a function to convert a column from vector to set, i.e., there is no to_set function.

How can I accomplish what I want, i.e., remove the duplicated elements from the vector?

Answer 1

You can convert the set function in python to a udf using functions.udf(set) and then apply it to the array column:

df.show()
+-------+
| skills|
+-------+
|a,a,b,c|
|  a,b,c|
|c,d,e,e|
+-------+

import pyspark.sql.functions as F
df.withColumn("unique_skills", F.udf(set)(F.split(df.skills, ","))).show()
+-------+-------------+
| skills|unique_skills|
+-------+-------------+
|a,a,b,c|    [a, b, c]|
|  a,b,c|    [a, b, c]|
|c,d,e,e|    [c, d, e]|
+-------+-------------+