Get a random boolean in python?

问题:

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?

回答1:

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

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

$ python -m timeit -s "import random" "random.choice([True, False])"
1000000 loops, best of 3: 0.904 usec per loop
$ python -m timeit -s "import random" "random.choice((True, False))" 
1000000 loops, best of 3: 0.846 usec per loop
$ python -m timeit -s "import random" "random.getrandbits(1)"
1000000 loops, best of 3: 0.286 usec per loop
$ python -m timeit -s "import random" "bool(random.getrandbits(1))"
1000000 loops, best of 3: 0.441 usec per loop
$ python -m timeit -s "import random" "not random.getrandbits(1)"
1000000 loops, best of 3: 0.308 usec per loop
$ python -m timeit -s "from random import getrandbits" "not getrandbits(1)"
1000000 loops, best of 3: 0.262 usec per loop  # not takes about 20us of this

Added this one after seeing @Pavel's answer

$ python -m timeit -s "from random import random" "random() < 0.5"
10000000 loops, best of 3: 0.115 usec per loop

回答2:

random.choice([True, False])

would also work.

回答3:

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

回答4:

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.

回答5:

I like

 np.random.rand() > .5

回答6:

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

回答7:

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)