random
— Generate pseudo-random numbers¶Source code: Lib/random.py
This module implements pseudo-random 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 in-place, 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 semi-open range [0.0, 1.0). Python
uses the Mersenne Twister as the core generator. It produces 53-bit precision
floats and has a period of 2**19937-1. 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 pseudo-random 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 623-dimensionally equidistributed uniform pseudorandom number generator”, ACM Transactions on Modeling and Computer Simulation Vol. 8, No. 1, January pp.3–30 1998.
Complementary-Multiply-with-Carry recipe for a compatible alternative random number generator with a long period and comparatively simple update operations.
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 the os.urandom()
function for details
on availability).
If a is an int, it is used directly.
With version 2 (the default), a str
, bytes
, or bytearray
object gets converted to an int
and all of its bits are used.
With version 1 (provided for reproducing random sequences from older versions
of Python), the algorithm for str
and bytes
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()
, and
setstate()
restores the internal state of the generator to what it was at
the time getstate()
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()
enables
randrange()
to handle arbitrarily large ranges.
random.
randrange
(stop)¶random.
randrange
(start, stop[, step])Return a randomly selected element from range(start, stop, step)
. This is
equivalent to choice(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 like int(random()*n)
which could produce
slightly uneven distributions.
random.
randint
(a, b)¶Return a random integer N such that a <= N <= b
. Alias for
randrange(a, b+1)
.
random.
choice
(seq)¶Return a random element from the non-empty 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 by random()
(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 0-argument 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 sub-slices 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.
The following functions generate specific real-valued 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
for
a <= b
and b <= N <= a
for b < a
.
The end-point value b
may or may not be included in the range
depending on floating-point rounding in the equation a + (b-a) * 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
and
beta > 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
and beta > 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.
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,
the seed()
method has no effect and is ignored.
The getstate()
and setstate()
methods raise
NotImplementedError
if called.
Sometimes it is useful to be able to reproduce the sequences given by a pseudo random number generator. By re-using 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:
random()
method will continue to produce the same
sequence when the compatible seeder is given the same seed.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 ten-value
>>> # (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/Example-Of-Bootstrapping.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 p-value 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 one-sided p-value 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 cross-validation.
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.