np.random.choice without replacement or weights is less efficient than random.sample?

I was interested in choosing k elements out of n without replacement or weighted probabilities, and with k<<n.

I've tested using the following ipython notebook: source / view.

The outcome was that using random.sample(xrange(n),k) was ~50-fold faster than using np.random.permutation(n)[:k], which is the way it is currently coded.