Why is numpy.random.choice so slow?

Question

While writing a script I discovered the numpy.random.choice function. I implemented it because it was was much cleaner than the equivalent if statement. However, after running the script I realized it is significantly slower than the if statement.

The following is a MWE. The first method takes 0.0 s, while the second takes 7.2 s. If you scale up the i loop, you will see how fast random.choice slows down.

Can anyone comment on why random.choice is so much slower?

import numpy as np
import numpy.random as rand
import time as tm

#-------------------------------------------------------------------------------

tStart = tm.time()
for i in xrange(100):
    for j in xrange(1000):
        tmp = rand.rand()
        if tmp < 0.25:
            var = 1
        elif tmp < 0.5:
            var = -1
print('Time: %.1f s' %(tm.time() - tStart))

#-------------------------------------------------------------------------------

tStart = tm.time()
for i in xrange(100):
    for j in xrange(1000):
        var = rand.choice([-1, 0, 1], p = [0.25, 0.5, 0.25])
print('Time: %.1f s' %(tm.time() - tStart))

Answer 1

You're using it wrong. Vectorize the operation, or numpy will offer no benefit:

var = numpy.random.choice([-1, 0, 1], size=1000, p=[0.25, 0.5, 0.25])

Timing data:

>>> timeit.timeit('''numpy.random.choice([-1, 0, 1],
...                                      size=1000,
...                                      p=[0.25, 0.5, 0.25])''',
...               'import numpy', number=10000)
2.380380242513752

>>> timeit.timeit('''
... var = []
... for i in xrange(1000):
...     tmp = rand.rand()
...     if tmp < 0.25:
...         var.append(1)
...     elif tmp < 0.5:
...         var.append(-1)
...     else:
...         var.append(0)''',
... setup='import numpy.random as rand', number=10000)
5.673041396894519