`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