For repeating array elements k times in PySpark we can use the below library.
Library : pyspark.sql.functions.array_repeat
array_repeat is a collection function that creates an array containing a column repeated count times.
Example
from pyspark.sql import SparkSession
from pyspark.sql.functions import array_repeat
df = spark.createDataFrame([('a1','a2','a3'),('b1','b2','b3'),\
('c1','c2','c3'),('d1','d2','d3'),\
('e1','e2','e3')], ['data1','data2','data3'])
df.show()
+-----+-----+-----+
|data1|data2|data3|
+-----+-----+-----+
| a1| a2| a3|
| b1| b2| b3|
| c1| c2| c3|
| d1| d2| d3|
| e1| e2| e3|
+-----+-----+-----+
df2 = df.select(df.data1,df.data2,df.data3,\
array_repeat(df.data3, 3).alias('repeat_data3'),\
array_repeat(df.data1, 2).alias('repeat_data1'))
df2.show()
+-----+-----+-----+------------+------------+
|data1|data2|data3|repeat_data3|repeat_data1|
+-----+-----+-----+------------+------------+
| a1| a2| a3|[a3, a3, a3]| [a1, a1]|
| b1| b2| b3|[b3, b3, b3]| [b1, b1]|
| c1| c2| c3|[c3, c3, c3]| [c1, c1]|
| d1| d2| d3|[d3, d3, d3]| [d1, d1]|
| e1| e2| e3|[e3, e3, e3]| [e1, e1]|
+-----+-----+-----+------------+------------+
