Simple Numpy example in Spark

spark_numpy.py

import numpy as np
 
from pyspark import SparkContext
from pyspark import SparkConf
 
conf = SparkConf()
conf.setMaster("spark://<HOSTNAME>:7077")
conf.setAppName("NumpyMult")
sc = SparkContext(conf=conf)
 
def mult(x):
    y = np.array([2])
    return x*y
 
x = np.arange(10000)
distData = sc.parallelize(x)
 
results = distData.map(mult).collect()
 
print results

Hi,
Thanks for the code snippet. I was wondering if just like distData, we can have another distData2 and do operations on both of them together?
To be more precise:
x = np.arrange(10000)
distData = sc.parallelize(x)

y = np.arrange(10000)
distData2 = sc.parallelize(y)

Now do array operations on both disData and distData2. Is this possible?

Thanks
Venkata D.