Passing Array to Python Spark Lit Function

Question

Let's say I have a numpy array a that contains the numbers 1-10. So a is [1 2 3 4 5 6 7 8 9 10].

Now, I also have a Python Spark dataframe to which I want to add my numpy array a. I figure that a column of literals will do the job. So I do the following:

df = df.withColumn("NewColumn", F.lit(a))

This doesn't work. The error is "Unsupported literal type class java.util.ArrayList".

Now, if I try just one element of the array, as follows, it works.

df = df.withColumn("NewColumn", F.lit(a[0]))

Is there a way I can do what I'm trying? I've been working on the task I want to complete for days and this is the closest I've come to finishing it. I have looked at all related Stack Overflow questions but I didn't get quite the answer I was looking for. Any help is appreciated. Thanks.

Answer 1

for loop in array inbuilt function

You can use array inbuilt function as

a = [1,2,3,4,5,6,7,8,9,10]
df = spark.createDataFrame([['a b c d e f g h i j '],], ['col1'])
df = df.withColumn("NewColumn", F.array([F.lit(x) for x in a]))
df.show(truncate=False)

You should get

+--------------------+-------------------------------+
|col1                |NewColumn                      |
+--------------------+-------------------------------+
|a b c d e f g h i j |[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]|
+--------------------+-------------------------------+
root
 |-- col1: string (nullable = true)
 |-- NewColumn: array (nullable = false)
 |    |-- element: integer (containsNull = false)

Using udf function

#udf function
def arrayUdf():
    return a
callArrayUdf = F.udf(arrayUdf, T.ArrayType(T.IntegerType()))

#calling udf function
df = df.withColumn("NewColumn", callArrayUdf())

output is same as with for loop way

Updated

I am pasting @pault's comment given below

You can hide the loop using map: df.withColumn("NewColumn", F.array(map(F.lit, a)))