random
— Generate pseudorandom numbers¶
Source code: Lib/random.py
This module implements pseudorandom number generators for various distributions.
For integers, there is uniform selection from a range. For sequences, there is uniform selection of a random element, a function to generate a random permutation of a list inplace, and a function for random sampling without replacement.
On the real line, there are functions to compute uniform, normal (Gaussian), lognormal, negative exponential, gamma, and beta distributions. For generating distributions of angles, the von Mises distribution is available.
Almost all module functions depend on the basic function random()
, which
generates a random float uniformly in the semiopen range [0.0, 1.0). Python
uses the Mersenne Twister as the core generator. It produces 53bit precision
floats and has a period of 2**199371. The underlying implementation in C is
both fast and threadsafe. The Mersenne Twister is one of the most extensively
tested random number generators in existence. However, being completely
deterministic, it is not suitable for all purposes, and is completely unsuitable
for cryptographic purposes.
The functions supplied by this module are actually bound methods of a hidden
instance of the random.Random
class. You can instantiate your own
instances of Random
to get generators that don’t share state.
Class Random
can also be subclassed if you want to use a different
basic generator of your own devising: in that case, override the random()
,
seed()
, getstate()
, and setstate()
methods.
Optionally, a new generator can supply a getrandbits()
method — this
allows randrange()
to produce selections over an arbitrarily large range.
The random
module also provides the SystemRandom
class which
uses the system function os.urandom()
to generate random numbers
from sources provided by the operating system.
Warning
The pseudorandom generators of this module should not be used for
security purposes. For security or cryptographic uses, see the
secrets
module.
See also
M. Matsumoto and T. Nishimura, “Mersenne Twister: A 623dimensionally equidistributed uniform pseudorandom number generator”, ACM Transactions on Modeling and Computer Simulation Vol. 8, No. 1, January pp.3–30 1998.
ComplementaryMultiplywithCarry recipe for a compatible alternative random number generator with a long period and comparatively simple update operations.
Bookkeeping functions¶

random.
seed
(a=None, version=2)¶ Initialize the random number generator.
If a is omitted or
None
, the current system time is used. If randomness sources are provided by the operating system, they are used instead of the system time (see theos.urandom()
function for details on availability).If a is an int, it is used directly.
With version 2 (the default), a
str
,bytes
, orbytearray
object gets converted to anint
and all of its bits are used.With version 1 (provided for reproducing random sequences from older versions of Python), the algorithm for
str
andbytes
generates a narrower range of seeds.Changed in version 3.2: Moved to the version 2 scheme which uses all of the bits in a string seed.

random.
getstate
()¶ Return an object capturing the current internal state of the generator. This object can be passed to
setstate()
to restore the state.

random.
setstate
(state)¶ state should have been obtained from a previous call to
getstate()
, andsetstate()
restores the internal state of the generator to what it was at the timegetstate()
was called.

random.
getrandbits
(k)¶ Returns a Python integer with k random bits. This method is supplied with the MersenneTwister generator and some other generators may also provide it as an optional part of the API. When available,
getrandbits()
enablesrandrange()
to handle arbitrarily large ranges.
Functions for integers¶

random.
randrange
(stop)¶ 
random.
randrange
(start, stop[, step]) Return a randomly selected element from
range(start, stop, step)
. This is equivalent tochoice(range(start, stop, step))
, but doesn’t actually build a range object.The positional argument pattern matches that of
range()
. Keyword arguments should not be used because the function may use them in unexpected ways.Changed in version 3.2:
randrange()
is more sophisticated about producing equally distributed values. Formerly it used a style likeint(random()*n)
which could produce slightly uneven distributions.

random.
randint
(a, b)¶ Return a random integer N such that
a <= N <= b
. Alias forrandrange(a, b+1)
.
Functions for sequences¶

random.
choice
(seq)¶ Return a random element from the nonempty sequence seq. If seq is empty, raises
IndexError
.

random.
choices
(population, weights=None, *, cum_weights=None, k=1)¶ Return a k sized list of elements chosen from the population with replacement. If the population is empty, raises
IndexError
.If a weights sequence is specified, selections are made according to the relative weights. Alternatively, if a cum_weights sequence is given, the selections are made according to the cumulative weights (perhaps computed using
itertools.accumulate()
). For example, the relative weights[10, 5, 30, 5]
are equivalent to the cumulative weights[10, 15, 45, 50]
. Internally, the relative weights are converted to cumulative weights before making selections, so supplying the cumulative weights saves work.If neither weights nor cum_weights are specified, selections are made with equal probability. If a weights sequence is supplied, it must be the same length as the population sequence. It is a
TypeError
to specify both weights and cum_weights.The weights or cum_weights can use any numeric type that interoperates with the
float
values returned byrandom()
(that includes integers, floats, and fractions but excludes decimals).New in version 3.6.

random.
shuffle
(x[, random])¶ Shuffle the sequence x in place.
The optional argument random is a 0argument function returning a random float in [0.0, 1.0); by default, this is the function
random()
.To shuffle an immutable sequence and return a new shuffled list, use
sample(x, k=len(x))
instead.Note that even for small
len(x)
, the total number of permutations of x can quickly grow larger than the period of most random number generators. This implies that most permutations of a long sequence can never be generated. For example, a sequence of length 2080 is the largest that can fit within the period of the Mersenne Twister random number generator.

random.
sample
(population, k)¶ Return a k length list of unique elements chosen from the population sequence or set. Used for random sampling without replacement.
Returns a new list containing elements from the population while leaving the original population unchanged. The resulting list is in selection order so that all subslices will also be valid random samples. This allows raffle winners (the sample) to be partitioned into grand prize and second place winners (the subslices).
Members of the population need not be hashable or unique. If the population contains repeats, then each occurrence is a possible selection in the sample.
To choose a sample from a range of integers, use a
range()
object as an argument. This is especially fast and space efficient for sampling from a large population:sample(range(10000000), k=60)
.If the sample size is larger than the population size, a
ValueError
is raised.
Realvalued distributions¶
The following functions generate specific realvalued distributions. Function parameters are named after the corresponding variables in the distribution’s equation, as used in common mathematical practice; most of these equations can be found in any statistics text.

random.
random
()¶ Return the next random floating point number in the range [0.0, 1.0).

random.
uniform
(a, b)¶ Return a random floating point number N such that
a <= N <= b
fora <= b
andb <= N <= a
forb < a
.The endpoint value
b
may or may not be included in the range depending on floatingpoint rounding in the equationa + (ba) * random()
.

random.
triangular
(low, high, mode)¶ Return a random floating point number N such that
low <= N <= high
and with the specified mode between those bounds. The low and high bounds default to zero and one. The mode argument defaults to the midpoint between the bounds, giving a symmetric distribution.

random.
betavariate
(alpha, beta)¶ Beta distribution. Conditions on the parameters are
alpha > 0
andbeta > 0
. Returned values range between 0 and 1.

random.
expovariate
(lambd)¶ Exponential distribution. lambd is 1.0 divided by the desired mean. It should be nonzero. (The parameter would be called “lambda”, but that is a reserved word in Python.) Returned values range from 0 to positive infinity if lambd is positive, and from negative infinity to 0 if lambd is negative.

random.
gammavariate
(alpha, beta)¶ Gamma distribution. (Not the gamma function!) Conditions on the parameters are
alpha > 0
andbeta > 0
.The probability distribution function is:
x ** (alpha  1) * math.exp(x / beta) pdf(x) =  math.gamma(alpha) * beta ** alpha

random.
gauss
(mu, sigma)¶ Gaussian distribution. mu is the mean, and sigma is the standard deviation. This is slightly faster than the
normalvariate()
function defined below.

random.
lognormvariate
(mu, sigma)¶ Log normal distribution. If you take the natural logarithm of this distribution, you’ll get a normal distribution with mean mu and standard deviation sigma. mu can have any value, and sigma must be greater than zero.

random.
normalvariate
(mu, sigma)¶ Normal distribution. mu is the mean, and sigma is the standard deviation.

random.
vonmisesvariate
(mu, kappa)¶ mu is the mean angle, expressed in radians between 0 and 2*pi, and kappa is the concentration parameter, which must be greater than or equal to zero. If kappa is equal to zero, this distribution reduces to a uniform random angle over the range 0 to 2*pi.

random.
paretovariate
(alpha)¶ Pareto distribution. alpha is the shape parameter.

random.
weibullvariate
(alpha, beta)¶ Weibull distribution. alpha is the scale parameter and beta is the shape parameter.
Alternative Generator¶

class
random.
SystemRandom
([seed])¶ Class that uses the
os.urandom()
function for generating random numbers from sources provided by the operating system. Not available on all systems. Does not rely on software state, and sequences are not reproducible. Accordingly, theseed()
method has no effect and is ignored. Thegetstate()
andsetstate()
methods raiseNotImplementedError
if called.
Notes on Reproducibility¶
Sometimes it is useful to be able to reproduce the sequences given by a pseudo random number generator. By reusing a seed value, the same sequence should be reproducible from run to run as long as multiple threads are not running.
Most of the random module’s algorithms and seeding functions are subject to change across Python versions, but two aspects are guaranteed not to change:
 If a new seeding method is added, then a backward compatible seeder will be offered.
 The generator’s
random()
method will continue to produce the same sequence when the compatible seeder is given the same seed.
Examples and Recipes¶
Basic examples:
>>> random() # Random float: 0.0 <= x < 1.0
0.37444887175646646
>>> uniform(2.5, 10.0) # Random float: 2.5 <= x < 10.0
3.1800146073117523
>>> expovariate(1 / 5) # Interval between arrivals averaging 5 seconds
5.148957571865031
>>> randrange(10) # Integer from 0 to 9 inclusive
7
>>> randrange(0, 101, 2) # Even integer from 0 to 100 inclusive
26
>>> choice(['win', 'lose', 'draw']) # Single random element from a sequence
'draw'
>>> deck = 'ace two three four'.split()
>>> shuffle(deck) # Shuffle a list
>>> deck
['four', 'two', 'ace', 'three']
>>> sample([10, 20, 30, 40, 50], k=4) # Four samples without replacement
[40, 10, 50, 30]
Simulations:
>>> # Six roulette wheel spins (weighted sampling with replacement)
>>> choices(['red', 'black', 'green'], [18, 18, 2], k=6)
['red', 'green', 'black', 'black', 'red', 'black']
>>> # Deal 20 cards without replacement from a deck of 52 playing cards
>>> # and determine the proportion of cards with a tenvalue
>>> # (a ten, jack, queen, or king).
>>> deck = collections.Counter(tens=16, low_cards=36)
>>> seen = sample(list(deck.elements()), k=20)
>>> seen.count('tens') / 20
0.15
>>> # Estimate the probability of getting 5 or more heads from 7 spins
>>> # of a biased coin that settles on heads 60% of the time.
>>> trial = lambda: choices('HT', cum_weights=(0.60, 1.00), k=7).count('H') >= 5
>>> sum(trial() for i in range(10000)) / 10000
0.4169
>>> # Probability of the median of 5 samples being in middle two quartiles
>>> trial = lambda : 2500 <= sorted(choices(range(10000), k=5))[2] < 7500
>>> sum(trial() for i in range(10000)) / 10000
0.7958
Example of statistical bootstrapping using resampling with replacement to estimate a confidence interval for the mean of a sample of size five:
# http://statistics.about.com/od/Applications/a/ExampleOfBootstrapping.htm
from statistics import mean
from random import choices
data = 1, 2, 4, 4, 10
means = sorted(mean(choices(data, k=5)) for i in range(20))
print(f'The sample mean of {mean(data):.1f} has a 90% confidence '
f'interval from {means[1]:.1f} to {means[2]:.1f}')
Example of a resampling permutation test to determine the statistical significance or pvalue of an observed difference between the effects of a drug versus a placebo:
# Example from "Statistics is Easy" by Dennis Shasha and Manda Wilson
from statistics import mean
from random import shuffle
drug = [54, 73, 53, 70, 73, 68, 52, 65, 65]
placebo = [54, 51, 58, 44, 55, 52, 42, 47, 58, 46]
observed_diff = mean(drug)  mean(placebo)
n = 10000
count = 0
combined = drug + placebo
for i in range(n):
shuffle(combined)
new_diff = mean(combined[:len(drug)])  mean(combined[len(drug):])
count += (new_diff >= observed_diff)
print(f'{n} label reshufflings produced only {count} instances with a difference')
print(f'at least as extreme as the observed difference of {observed_diff:.1f}.')
print(f'The onesided pvalue of {count / n:.4f} leads us to reject the null')
print(f'hypothesis that there is no difference between the drug and the placebo.')
Simulation of arrival times and service deliveries in a single server queue:
from random import expovariate, gauss
from statistics import mean, median, stdev
average_arrival_interval = 5.6
average_service_time = 5.0
stdev_service_time = 0.5
num_waiting = 0
arrivals = []
starts = []
arrival = service_end = 0.0
for i in range(20000):
if arrival <= service_end:
num_waiting += 1
arrival += expovariate(1.0 / average_arrival_interval)
arrivals.append(arrival)
else:
num_waiting = 1
service_start = service_end if num_waiting else arrival
service_time = gauss(average_service_time, stdev_service_time)
service_end = service_start + service_time
starts.append(service_start)
waits = [start  arrival for arrival, start in zip(arrivals, starts)]
print(f'Mean wait: {mean(waits):.1f}. Stdev wait: {stdev(waits):.1f}.')
print(f'Median wait: {median(waits):.1f}. Max wait: {max(waits):.1f}.')
See also
Statistics for Hackers a video tutorial by Jake Vanderplas on statistical analysis using just a few fundamental concepts including simulation, sampling, shuffling, and crossvalidation.
Economics Simulation a simulation of a marketplace by Peter Norvig that shows effective use of many of the tools and distributions provided by this module (gauss, uniform, sample, betavariate, choice, triangular, and randrange).
A Concrete Introduction to Probability (using Python) a tutorial by Peter Norvig covering the basics of probability theory, how to write simulations, and how to perform data analysis using Python.