Pyspark replace values on array column based on another dataframe

Question

I have two dataframes, one simply with some unique ids with associated names like so:

Id name
0 name_a
1 name_b
2 name_c

Second dataframe contains the ids from the first dataframe stored in an array, in each row:

Row_1 row_2
0 [0,2]
1 [1,0]

My question is it possible to replace the arrays from the second dataframe so it checks the names from the first df based on the ids, so:

Row_1 row_2
0 [name_a, name_c]
1 [name_b, name_a]

It seems too time consuming to create a map of the first df and just add it to the second df with an udf. Any help is much appreciated on how to approach this.

Answer 1

Join using array_contains function + groupby and collect_list:

from pyspark.sql import functions as F

df1 = spark.createDataFrame([(0, "name_a"), (1, "name_b"), (2, "name_c")], ["Id", "name"])
df2 = spark.createDataFrame([(0, [0, 2]), (1, [1, 0])], ["Row_1", "Row_2"])

result = df2.join(
    df1, on=F.array_contains("Row_2", F.col("Id")), how="left"
).groupBy("Row_1").agg(
    F.collect_list("name").alias("Row_2")
)

result.show()
#+-----+----------------+
#|Row_1|           Row_2|
#+-----+----------------+
#|    0|[name_a, name_c]|
#|    1|[name_a, name_b]|
#+-----+----------------+

Answer 2

You can try using explode function to convert array into rows, then join the data with the initial data frame, in the last step do a group by & .agg(collect_list())

from pyspark.sql.functions import explode

df3 = df2.select(df2.row_1,explode(df2.row_2))
df4 = df3.join(df1,df3.row_1==df1.Id).select(df3.row_1,df1.name)
df5 = df4.groupBy('row_1').agg(collect_list('name').alias('name'))

Reference links:

1. https://sparkbyexamples.com/pyspark/pyspark-explode-array-and-map-columns-to-rows/#:~:text=explode%20%E2%80%93%20PySpark%20explode%20array%20or,it%20contains%20all%20array%20elements.
2. https://www.owenrumney.co.uk/pyspark-opposite-of-explode/