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

I also have a Spark dataframe to which I want to add my numpy array a. I figure that a column of literals will do the job. This doesn't work:

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

Unsupported literal type class java.util.ArrayList

But this works:

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

How to do it?

Example DF before:

Expected result:

List comprehension inside Spark's array

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)
df.printSchema()
#  +--------------------+-------------------------------+
#  |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)

@pault commented (Python 2.7):

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

@ abegehr added Python 3 version:

df.withColumn("NewColumn", F.array(*map(F.lit, a)))

Spark's udf

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

# Calling UDF
df = df.withColumn("NewColumn", callArrayUdf())

Output is the same.