PySpark equivalent of function "typedLit" from Scala API

Question

We have a function typedLit in Scala API for Spark to add the Array or Map as column value.

import org.apache.spark.sql.functions.typedLit
val df1 = Seq((1, 0), (2, 3)).toDF("a", "b")

df1.withColumn("seq", typedLit(Seq(1,2,3)))
    .show(truncate=false)

+---+---+---------+
|a  |b  |seq      |
+---+---+---------+
|1  |0  |[1, 2, 3]|
|2  |3  |[1, 2, 3]|
+---+---+---------+

I couldn't find the equivalent in PySpark. How can we create a column in PySpark with Array as a column value?

Answer 1

There isn't an equivalent function in pyspark yet, but you can have an array column as shown below:

from pyspark.sql.functions import array, lit
df = sc.parallelize([[1,2], [3,4]]).toDF(['a', 'b'])
df.withColumn('seq', array([lit(i) for i in [1,2,3]])).show()

Output:

+---+---+---------+                                                             
|  a|  b|      seq|
+---+---+---------+
|  1|  2|[1, 2, 3]|
|  3|  4|[1, 2, 3]|
+---+---+---------+