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📛 NAME

random - Random variable generators.

🚀 Quick Reference

Use Case	Command	Description
🎲 Random float	random.random()	Uniform float in [0.0, 1.0)
🔢 Random integer	random.randint(a, b)	Integer inclusive of both ends
📏 Random from range	random.randrange(start, stop[, step])	Random item from range, exclusive of stop
🎯 Random choice	random.choice(seq)	Single random element from sequence
🃏 Weighted choice	random.choices(pop, weights=..., k=1)	k elements with replacement
🔀 Shuffle	random.shuffle(x)	Shuffle list in place
📋 Sample	random.sample(pop, k)	k unique elements without replacement
🌱 Seed	random.seed(a)	Initialize generator state
📦 Random bytes	random.randbytes(n)	n random bytes
📊 Uniform distribution	random.uniform(a, b)	Float in [a, b) or [a, b]
📈 Gaussian	random.gauss(mu, sigma)	Normal distribution (faster)
📉 Exponential	random.expovariate(lambd)	Exponential distribution

🔗 MODULE REFERENCE

https://docs.python.org/3.10/library/random.html

The following documentation is automatically generated from the Python source files. It may be incomplete, incorrect or include features that are considered implementation detail and may vary between Python implementations. When in doubt, consult the module reference at the location listed above.

📝 DESCRIPTION

bytes

• uniform bytes (values between 0 and 255)

integers

• uniform within range

sequences

• pick random element
• pick random sample
• pick weighted random sample
• generate random permutation

distributions on the real line:

• uniform
• triangular
• normal (Gaussian)
• lognormal
• negative exponential
• gamma
• beta
• pareto
• Weibull

distributions on the circle (angles 0 to 2pi):

• circular uniform
• von Mises

General notes on the underlying Mersenne Twister core generator:

• The period is 2**19937-1.
• It is one of the most extensively tested generators in existence.
• The random() method is implemented in C, executes in a single Python step, and is, therefore, threadsafe.

📚 CLASSES

_random.Random(builtins.object)
    Random
        SystemRandom

🎲 class Random(_random.Random)

Random(x=None)

Random number generator base class used by bound module functions.

Used to instantiate 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 following methods: random(), seed(), getstate(), and setstate(). Optionally, implement a getrandbits() method so that randrange() can cover arbitrarily large ranges.

Method resolution order:

• Random
• _random.Random
• builtins.object

Methods defined here:

• __getstate__(self) — # Issue 17489: Since __reduce__ was defined to fix #759889 this is no longer called; we leave it here because it has been here since random was rewritten back in 2001 and why risk breaking something.
• __init__(self, x=None) — Initialize an instance. Optional argument x controls seeding, as for Random.seed().
• __reduce__(self) — Helper for pickle.
• __setstate__(self, state)
• betavariate(self, alpha, beta) — Beta distribution. Conditions on the parameters are alpha > 0 and beta > 0. Returned values range between 0 and 1.
• choice(self, seq) — Choose a random element from a non-empty sequence.
• choices(self, population, weights=None, *, cum_weights=None, k=1) — Return a k sized list of population elements chosen with replacement. If the relative weights or cumulative weights are not specified, the selections are made with equal probability.
• expovariate(self, 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.
• gammavariate(self, 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

• gauss(self, mu, sigma) — Gaussian distribution. mu is the mean, and sigma is the standard deviation. This is slightly faster than the normalvariate() function. Not thread-safe without a lock around calls.
• getstate(self) — Return internal state; can be passed to setstate() later.
• lognormvariate(self, 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.
• normalvariate(self, mu, sigma) — Normal distribution. mu is the mean, and sigma is the standard deviation.
• paretovariate(self, alpha) — Pareto distribution. alpha is the shape parameter.
• randbytes(self, n) — Generate n random bytes.
• randint(self, a, b) — Return random integer in range [a, b], including both end points.
• randrange(self, start, stop=None, step=1) — Choose a random item from range(start, stop[, step]). This fixes the problem with randint() which includes the endpoint; in Python this is usually not what you want.
• sample(self, population, k, *, counts=None) — Chooses k unique random elements from a population sequence or set. 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 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 range() for the population argument. This is especially fast and space efficient for sampling from a large population:

  sample(range(10000000), 60)

• seed(self, a=None, version=2) — Initialize internal state from a seed. The only supported seed types are None, int, float, str, bytes, and bytearray. None or no argument seeds from current time or from an operating system specific randomness source if available. If *a* is an int, all bits are used. For version 2 (the default), all of the bits are used if *a* is a str, bytes, or bytearray. For version 1 (provided for reproducing random sequences from older versions of Python), the algorithm for str and bytes generates a narrower range of seeds.
• setstate(self, state) — Restore internal state from object returned by getstate().
• shuffle(self, x, random=None) — Shuffle list x in place, and return None. Optional argument random is a 0-argument function returning a random float in [0.0, 1.0); if it is the default None, the standard random.random will be used.
• triangular(self, low=0.0, high=1.0, mode=None) — Triangular distribution. Continuous distribution bounded by given lower and upper limits, and having a given mode value in-between. http://en.wikipedia.org/wiki/Triangular_distribution
• uniform(self, a, b) — Get a random number in the range [a, b) or [a, b] depending on rounding.
• vonmisesvariate(self, mu, kappa) — Circular data distribution. 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.
• weibullvariate(self, alpha, beta) — Weibull distribution. alpha is the scale parameter and beta is the shape parameter.

Class methods defined here:

• __init_subclass__(**kwargs) from builtins.type — Control how subclasses generate random integers. The algorithm a subclass can use depends on the random() and/or getrandbits() implementation available to it and determines whether it can generate random integers from arbitrarily large ranges.

Data descriptors defined here:

• __dict__ — dictionary for instance variables (if defined)
• __weakref__ — list of weak references to the object (if defined)

Data and other attributes defined here:

• VERSION = 3

Methods inherited from _random.Random:

• getrandbits(self, k, /) — getrandbits(k) -> x. Generates an int with k random bits.
• random(self, /) — random() -> x in the interval [0, 1).

Static methods inherited from _random.Random:

• __new__(*args, **kwargs) from builtins.type — Create and return a new object. See help(type) for accurate signature.

🔒 class SystemRandom(Random)

SystemRandom(x=None)

Alternate random number generator using sources provided by the operating system (such as /dev/urandom on Unix or CryptGenRandom on Windows). Not available on all systems (see os.urandom() for details).

Method resolution order:

• SystemRandom
• Random
• _random.Random
• builtins.object

Methods defined here:

• getrandbits(self, k) — getrandbits(k) -> x. Generates an int with k random bits.
• getstate = _notimplemented(self, *args, **kwds)
• randbytes(self, n) — Generate n random bytes.
• random(self) — Get the next random number in the range [0.0, 1.0).
• seed(self, *args, **kwds) — Stub method. Not used for a system random number generator.
• setstate = _notimplemented(self, *args, **kwds)

Inherits all other methods from Random (see above).

📦 FUNCTIONS

The following are module-level functions bound to a default Random instance. See the corresponding Random method documentation for full details.

• betavariate(alpha, beta) — Beta distribution. (See Random.betavariate)
• choice(seq) — Choose a random element from a non-empty sequence. (See Random.choice)
• choices(population, weights=None, *, cum_weights=None, k=1) — Return a k sized list of population elements chosen with replacement. (See Random.choices)
• expovariate(lambd) — Exponential distribution. (See Random.expovariate)
• gammavariate(alpha, beta) — Gamma distribution. (See Random.gammavariate)
• gauss(mu, sigma) — Gaussian distribution. (See Random.gauss)
• getrandbits(k, /) — getrandbits(k) -> x. Generates an int with k random bits. (See Random.getrandbits)
• getstate() — Return internal state; can be passed to setstate() later. (See Random.getstate)
• lognormvariate(mu, sigma) — Log normal distribution. (See Random.lognormvariate)
• normalvariate(mu, sigma) — Normal distribution. (See Random.normalvariate)
• paretovariate(alpha) — Pareto distribution. (See Random.paretovariate)
• randbytes(n) — Generate n random bytes. (See Random.randbytes)
• randint(a, b) — Return random integer in range [a, b], including both end points. (See Random.randint)
• random() — random() -> x in the interval [0, 1). (See Random.random)
• randrange(start, stop=None, step=1) — Choose a random item from range(start, stop[, step]). (See Random.randrange)
• sample(population, k, *, counts=None) — Chooses k unique random elements from a population sequence or set. (See Random.sample)
• seed(a=None, version=2) — Initialize internal state from a seed. (See Random.seed)
• setstate(state) — Restore internal state from object returned by getstate(). (See Random.setstate)
• shuffle(x, random=None) — Shuffle list x in place, and return None. (See Random.shuffle)
• triangular(low=0.0, high=1.0, mode=None) — Triangular distribution. (See Random.triangular)
• uniform(a, b) — Get a random number in the range [a, b) or [a, b] depending on rounding. (See Random.uniform)
• vonmisesvariate(mu, kappa) — Circular data distribution. (See Random.vonmisesvariate)
• weibullvariate(alpha, beta) — Weibull distribution. (See Random.weibullvariate)

📊 DATA

__all__ = ['Random', 'SystemRandom', 'betavariate', 'choice', 'choices'...]

📁 FILE

/usr/lib/python3.10/random.py

📛 NAME 🚀 Quick Reference 🔗 MODULE REFERENCE 📝 DESCRIPTION 📚 CLASSES 📦 FUNCTIONS 📊 DATA 📁 FILE