Strange behavior of RDD.take(1) / RDD.first() in PySpark

Lets start with a simple function which always returns a random integer:

import numpy as np

def f(x):
    return np.random.randint(1000)

and a RDD filled with zeros and mapped using f:

rdd = sc.parallelize([0] * 10).map(f)

Since above RDD is not persisted I expect I'll get a different output every time I collect:

> rdd.collect()
[255, 512, 512, 512, 255, 512, 255, 512, 512, 255]

If we ignore the fact that distribution of values doesn't really look random it is more or less what happens. Problem starts we we when take only a first element:

assert len(set(rdd.first() for _ in xrange(100))) == 1

or

assert len(set(tuple(rdd.take(1)) for _ in xrange(100))) == 1

It seems to return the same number each time. I've been able to reproduce this behavior on two different machines with Spark 1.2, 1.3 and 1.4. Here I am using np.random.randint but it behaves the same way with random.randint.

This issue, same as non-exactly-random results with collect, seems to be Python specific and I couldn't reproduce it using Scala:

def f(x: Int) = scala.util.Random.nextInt(1000)

val rdd = sc.parallelize(List.fill(10)(0)).map(f)
(1 to 100).map(x => rdd.first).toSet.size

rdd.collect()

Do I miss something obvious here?