Get a random boolean in python?

Question

I am looking for the best way (fast and elegant) to get a random boolean in python (flip a coin).

For the moment I am using random.randint(0, 1) or random.getrandbits(1).

Are there better choices that I am not aware of?

This is a good and valid question about performance of various options for getting a random boolean, but I feel like lost in the noise of all the benchmarks is the fact that the best performer saves less than a second versus the worst performer over a million iterations. If you've come here looking for a way to speed up an application, you should probably look at other options first. — Ian McLaird
– Ian McLaird, Commented Oct 15, 2020 at 14:16

Andros Fenollosa · Accepted Answer · 2023-02-14 04:17:56Z

527

Adam's answer is quite fast, but I found that random.getrandbits(1) to be quite a lot faster. If you really want a boolean instead of a long then

import random
bool(random.getrandbits(1))

is still about twice as fast as random.choice([True, False])

Both solutions need to import random

If utmost speed isn't to priority then random.choice definitely reads better.

Note that random.choice() is slower than just choice() (after from random import choice) due to the attribute lookup.

$ python3 --version
Python 3.9.7
$ python3 -m timeit -s "from random import choice" "choice([True, False])"
1000000 loops, best of 5: 376 nsec per loop
$ python3 -m timeit -s "from random import choice" "choice((True, False))"
1000000 loops, best of 5: 352 nsec per loop
$ python3 -m timeit -s "from random import getrandbits" "getrandbits(1)"
10000000 loops, best of 5: 33.7 nsec per loop
$ python3 -m timeit -s "from random import getrandbits" "bool(getrandbits(1))"
5000000 loops, best of 5: 89.5 nsec per loop
$ python3 -m timeit -s "from random import getrandbits" "not getrandbits(1)"
5000000 loops, best of 5: 46.3 nsec per loop
$ python3 -m timeit -s "from random import random" "random() < 0.5"
5000000 loops, best of 5: 46.4 nsec per loop

edited Feb 14, 2023 at 4:17

Andros Fenollosa

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answered Jul 26, 2011 at 3:18

John La Rooy

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4 Comments

Michał Bentkowski Over a year ago

If we're all about performance, not not random.getrandbits(1)) is faster than bool ;)

Adam Vandenberg Over a year ago

You likely don't even need to cast to a boolean at all, since 0/1 have the proper truth values.

kindall Over a year ago

You could speed it up further by doing from random import getrandbits to avoid the attribute lookup. :-)

jl005 Over a year ago

actually, the missing attribute lookup seems to be the reason why random() < 0.5 is the fastest. When using from random import getrandbits getrandbits is faster than random() < 0.5.

castis · Accepted Answer · 2021-11-11 01:18:45Z

287

import random
random.choice([True, False])

would also work.

edited Nov 11, 2021 at 1:18

castis

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answered Jul 26, 2011 at 2:48

Adam Vandenberg

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4 Comments

hola Over a year ago

Do I need to import some library?

castis Over a year ago

Just random. It is part of the standard library

Karl Knechtel Over a year ago

Indeed - the same random used by most of the other answers here.

cdahms Over a year ago

I really like this answer, perhaps not the fastest answer here but definitely the most readable.

Pavel Radchenko · Accepted Answer · 2021-06-11 10:55:43Z

57

Found a faster method:

$ python -m timeit -s "from random import getrandbits" "not getrandbits(1)"
10000000 loops, best of 3: 0.222 usec per loop
$ python -m timeit -s "from random import random" "True if random() > 0.5 else False"
10000000 loops, best of 3: 0.0786 usec per loop
$ python -m timeit -s "from random import random" "random() < 0.5"
10000000 loops, best of 3: 0.0579 usec per loop

edited Jun 11, 2021 at 10:55

answered Mar 5, 2014 at 15:23

Pavel Radchenko

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5 Comments

John La Rooy Over a year ago

random() > 0.5 already evaluates to a bool which is even faster!

Simon Lindholm Over a year ago

random() >= 0.5, otherwise you will be a tiny bit biased towards False.

akxlr Over a year ago

random() < 0.5 makes more sense as changing 0.5 to some other probability works as expected

Pavel Radchenko Over a year ago

Changed to random() < 0.5, thanks @SimonLindholm and @akxlr.

Eric Duminil Over a year ago

True if random() > 0.5 else False Ahhhhh!!!!!!!! My eyes.

Maarten · Accepted Answer · 2013-09-22 08:22:48Z

14

I like

 np.random.rand() > .5

answered Sep 22, 2013 at 8:22

Maarten

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Comments

Chris · Accepted Answer · 2011-12-07 22:39:50Z

13

If you want to generate a number of random booleans you could use numpy's random module. From the documentation

np.random.randint(2, size=10)

will return 10 random uniform integers in the open interval [0,2). The size keyword specifies the number of values to generate.

answered Dec 7, 2011 at 22:39

Chris

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1 Comment

ojunk Over a year ago

I was curious as to how the speed of this method performed against the answers since this option was left out of the comparisons. To generate one random bool (which is the question) this is much slower but if you wanted to generate many then this mecomes much faster: $ python -m timeit -s "from random import random" "random() < 0.5" 10000000 loops, best of 3: 0.0906 usec per loop

Partiban · Accepted Answer · 2019-11-26 11:07:11Z

8

You could use the Faker library, it's mainly used for testing, but is capable of providing a variety of fake data.

Install: https://pypi.org/project/Faker/

>>> from faker import Faker
>>> fake = Faker()
>>> fake.pybool()
True

answered Nov 26, 2019 at 11:07

Partiban

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Comments

ojunk · Accepted Answer · 2018-08-08 10:09:58Z

I was curious as to how the speed of the numpy answer performed against the other answers since this was left out of the comparisons. To generate one random bool this is much slower but if you wanted to generate many then this becomes much faster:

$ python -m timeit -s "from random import random" "random() < 0.5"
10000000 loops, best of 3: 0.0906 usec per loop
$ python -m timeit -s "import numpy as np" "np.random.randint(2, size=1)"
100000 loops, best of 3: 4.65 usec per loop

$ python -m timeit -s "from random import random" "test = [random() < 0.5 for i in range(1000000)]"
10 loops, best of 3: 118 msec per loop
$ python -m timeit -s "import numpy as np" "test = np.random.randint(2, size=1000000)"
100 loops, best of 3: 6.31 msec per loop

Shawn Mehan · Accepted Answer · 2015-10-28 22:16:49Z

3

A new take on this question would involve the use of Faker which you can install easily with pip.

from faker import Factory

#----------------------------------------------------------------------
def create_values(fake):
    """"""
    print fake.boolean(chance_of_getting_true=50) # True
    print fake.random_int(min=0, max=1) # 1

if __name__ == "__main__":
    fake = Factory.create()
    create_values(fake)

edited Oct 28, 2015 at 22:16

Shawn Mehan

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answered Oct 28, 2015 at 21:34

Althea

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3 Comments

Martino Over a year ago

You should at least explain why you think this is a better solution, considering it involves downloading a different package and is messier.

Jason McVetta Over a year ago

I disagree with the downvotes. If you're creating random data, you may well be in a situation where Faker is a very useful tool. The fake.boolean() syntax is clean and easy for others to grok.

Apollys supports Monica Over a year ago

Regardless of whether or not the package is useful, the complete lack of explanation as to why one should consider this makes the answer useless.

gourav naik · Accepted Answer · 2020-12-23 23:15:37Z

3

u could try this it produces randomly generated array of true and false :

a=[bool(i) for i in np.array(np.random.randint(0,2,10))]

out: [True, True, True, True, True, False, True, False, True, False]

answered Dec 23, 2020 at 23:15

gourav naik

311 bronze badge

Comments

Javad · Accepted Answer · 2022-10-05 11:00:15Z

0

Below methods would also work for this purpose:

import random
random.choice([0, 1])

or

import random
random.choice(range(2))

answered Oct 5, 2022 at 11:00

Javad

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