# A weighted version of random.choice

Since version 1.7.0, NumPy has a `choice`

function that supports probability distributions.

`from numpy.random import choicedraw = choice(list_of_candidates, number_of_items_to_pick, p=probability_distribution)`

Note that `probability_distribution`

is a sequence in the same order of `list_of_candidates`

. You can also use the keyword `replace=False`

to change the behavior so that drawn items are not replaced.

Since Python 3.6 there is a method `choices`

from the `random`

module.

`In [1]: import randomIn [2]: random.choices(...: population=[['a','b'], ['b','a'], ['c','b']],...: weights=[0.2, 0.2, 0.6],...: k=10...: )Out[2]:[['c', 'b'], ['c', 'b'], ['b', 'a'], ['c', 'b'], ['c', 'b'], ['b', 'a'], ['c', 'b'], ['b', 'a'], ['c', 'b'], ['c', 'b']]`

Note that `random.choices`

will sample *with replacement*, per the docs:

Return a

`k`

sized list of elements chosen from the population with replacement.

Note for completeness of answer:

When a sampling unit is drawn from a finite population and is returnedto that population, after its characteristic(s) have been recorded,before the next unit is drawn, the sampling is said to be "withreplacement". It basically means each element may be chosen more thanonce.

If you need to sample without replacement, then as @ronan-paixão's brilliant answer states, you can use `numpy.choice`

, whose `replace`

argument controls such behaviour.

`def weighted_choice(choices): total = sum(w for c, w in choices) r = random.uniform(0, total) upto = 0 for c, w in choices: if upto + w >= r: return c upto += w assert False, "Shouldn't get here"`