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.
{tip}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.
Bookkeeping functions
random.
seed
(a=None, version=2)
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.
Deprecated since version 3.9: In the future, the seed must be one of the following types: NoneType, int
, float
, str
, bytes
, or bytearray
.
random.
getstate
()
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)
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.
Functions for bytes
random.
randbytes
(n)
random.
randbytes
(n)Generate n random bytes.
This method should not be used for generating security tokens. Use secrets.token_bytes()
instead.
New in version 3.9.
Functions for integers
random.
randrange
(stop)
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)
random.
randint
(a, b)Return a random integer N such that a <= N <= b
. Alias for randrange(a, b+1)
.
random.
getrandbits
(k)
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.
Changed in version 3.9: This method now accepts zero for k.
Functions for sequences
random.
choice
(seq)
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)
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). Behavior is undefined if any weight is negative. A ValueError
is raised if all weights are zero.
For a given seed, the choices()
function with equal weighting typically produces a different sequence than repeated calls to choice()
. The algorithm used by choices()
uses floating point arithmetic for internal consistency and speed. The algorithm used by choice()
defaults to integer arithmetic with repeated selections to avoid small biases from round-off error.
New in version 3.6.
Changed in version 3.9: Raises a ValueError
if all weights are zero.
random.
shuffle
(x[, random])
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.
Deprecated since version 3.9, will be removed in version 3.11: The optional parameter random.
random.
sample
(population, k, *, counts=None)
random.
sample
(population, k, *, counts=None)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.
Repeated elements can be specified one at a time or with the optional keyword-only counts parameter. For example, sample(['red', 'blue'], counts=[4, 2], k=5)
is equivalent to sample(['red', 'red', 'red', 'red', 'blue', 'blue'], k=5)
.
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.
Changed in version 3.9: Added the counts parameter.
Deprecated since version 3.9: In the future, the population must be a sequence. Instances of set
are no longer supported. The set must first be converted to a list
or tuple
, preferably in a deterministic order so that the sample is reproducible.
Real-valued distributions
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
()
random.
random
()Return the next random floating point number in the range [0.0, 1.0).
random.
uniform
(a, b)
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)
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)
random.
betavariate
(alpha, beta)Beta distribution. Conditions on the parameters are alpha > 0
and beta > 0
. Returned values range between 0 and 1.
Bookkeeping functions
1600 0 1 years ago
random.seed(a=None, version=2)Initialize the random number generator.If a is omitted or N...
1429 0 1 years ago
random.getstate()Return an object capturing the current internal state of the generator. ...
1405 0 1 years ago
random.setstate(state)state should have been obtained from a previous call to getstate(),...
1604 0 1 years ago
Functions for bytes
1008 0 1 years ago
random.randbytes(n)Generate n random bytes.This method should not be used for generating ...
926 0 1 years ago
Functions for integers
1109 0 1 years ago
random.randrange(stop)random.randrange(start, stop[, step])Return a randomly selected ele...
1083 0 1 years ago
random.randint(a, b)Return a random integer N such that a <= N <= b. Alias for randrange...
1194 0 1 years ago
random.getrandbits(k)Returns a Python integer with k random bits. This method is supplied...
1235 0 1 years ago
Functions for sequences
990 0 1 years ago
random.choice(seq)Return a random element from the non-empty sequence seq. If seq is empt...
1390 0 1 years ago
random.choices(population, weights=None, *, cum_weights=None, k=1)Return a k sized list o...
1340 0 1 years ago
random.shuffle(x[, random])Shuffle the sequence x in place.The optional argument random i...
1205 0 1 years ago
random.sample(population, k, *, counts=None)Return a k length list of unique elements cho...
640 0 1 years ago
Real-valued distributionsThe following functions generate specific real-valued distributio...
984 0 1 years ago
random.random()Return the next random floating point number in the range [0.0, 1.0).
130 0 1 years ago
random.uniform(a, b)Return a random floating point number N such that a <= N <= b for a <...
1212 0 1 years ago
random.triangular(low, high, mode)Return a random floating point number N such that low <...
1235 0 1 years ago
random.betavariate(alpha, beta)Beta distribution. Conditions on the parameters are alpha...
959 0 1 years ago