python and numpy: Get random distribution from existing distribution

Question

I have a given sample distribution like for example [1,2,3,4,6,10]. From this I would like to get a random list of samples where the values come from the initial samples. So basically I want the same samples just in a random order (so the number of samples in the random sample set remains the same). I can do this using random.sample() but I am not sure how random this really is.

Is there a similar functionality in numpy that I can use for scientific purpose?

Aren't you just shuffling list elements? shuffle()?

miradulo
– miradulo

2015-04-08 13:43:23 +00:00
Commented Apr 8, 2015 at 13:43 — miradulo
– miradulo, Commented Apr 8, 2015 at 13:43

Kasravnd · Accepted Answer · 2015-04-08 13:50:06Z

2

You can use random.shuffle :

>>> import random
>>> a=[1,2,3,4,6,10]
>>> random.shuffle(a)
>>> a
[1, 3, 6, 2, 10, 4]

And same in numpy with numpy.random.shuffle:

>>> import numpy as np
>>> np.random.shuffle(a)
>>> a
[4, 6, 10, 3, 1, 2]

edited Apr 8, 2015 at 13:50

answered Apr 8, 2015 at 13:43

Kasravnd

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