:mod:`random` --- Generate pseudo-random numbers ================================================ .. module:: random :synopsis: Generate pseudo-random numbers with various common distributions. This module implements pseudo-random number generators for various distributions. For integers, uniform selection from a range. For sequences, 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 :func:`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 :class:`random.Random` class. You can instantiate your own instances of :class:`Random` to get generators that don't share state. Class :class:`Random` can also be subclassed if you want to use a different basic generator of your own devising: in that case, override the :meth:`random`, :meth:`seed`, :meth:`getstate`, and :meth:`setstate`. Optionally, a new generator can supply a :meth:`getrandombits` method --- this allows :meth:`randrange` to produce selections over an arbitrarily large range. Bookkeeping functions: .. function:: seed([x]) Initialize the basic random number generator. Optional argument *x* can be any :term:`hashable` object. If *x* is omitted or ``None``, current system time is used; current system time is also used to initialize the generator when the module is first imported. If randomness sources are provided by the operating system, they are used instead of the system time (see the :func:`os.urandom` function for details on availability). If *x* is not ``None`` or an int, ``hash(x)`` is used instead. If *x* is an int, *x* is used directly. .. function:: getstate() Return an object capturing the current internal state of the generator. This object can be passed to :func:`setstate` to restore the state. State values produced in Python 2.6 cannot be loaded into earlier versions. .. function:: setstate(state) *state* should have been obtained from a previous call to :func:`getstate`, and :func:`setstate` restores the internal state of the generator to what it was at the time :func:`setstate` was called. .. function:: jumpahead(n) Change the internal state to one different from and likely far away from the current state. *n* is a non-negative integer which is used to scramble the current state vector. This is most useful in multi-threaded programs, in conjunction with multiple instances of the :class:`Random` class: :meth:`setstate` or :meth:`seed` can be used to force all instances into the same internal state, and then :meth:`jumpahead` can be used to force the instances' states far apart. .. function:: 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, :meth:`getrandbits` enables :meth:`randrange` to handle arbitrarily large ranges. Functions for integers: .. function:: 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. .. function:: randint(a, b) Return a random integer *N* such that ``a <= N <= b``. Functions for sequences: .. function:: choice(seq) Return a random element from the non-empty sequence *seq*. If *seq* is empty, raises :exc:`IndexError`. .. function:: 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 :func:`random`. Note that for even rather small ``len(x)``, the total number of permutations of *x* is larger than the period of most random number generators; this implies that most permutations of a long sequence can never be generated. .. function:: 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 :term:`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 an :func:`range` object as an argument. This is especially fast and space efficient for sampling from a large population: ``sample(range(10000000), 60)``. 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. .. function:: random() Return the next random floating point number in the range [0.0, 1.0). .. function:: uniform(a, b) Return a random floating point number *N* such that ``a <= N < b``. .. function:: betavariate(alpha, beta) Beta distribution. Conditions on the parameters are ``alpha > 0`` and ``beta > 0``. Returned values range between 0 and 1. .. function:: expovariate(lambd) Exponential distribution. *lambd* is 1.0 divided by the desired mean. (The parameter would be called "lambda", but that is a reserved word in Python.) Returned values range from 0 to positive infinity. .. function:: gammavariate(alpha, beta) Gamma distribution. (*Not* the gamma function!) Conditions on the parameters are ``alpha > 0`` and ``beta > 0``. .. function:: gauss(mu, sigma) Gaussian distribution. *mu* is the mean, and *sigma* is the standard deviation. This is slightly faster than the :func:`normalvariate` function defined below. .. function:: 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. .. function:: normalvariate(mu, sigma) Normal distribution. *mu* is the mean, and *sigma* is the standard deviation. .. function:: 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*. .. function:: paretovariate(alpha) Pareto distribution. *alpha* is the shape parameter. .. function:: weibullvariate(alpha, beta) Weibull distribution. *alpha* is the scale parameter and *beta* is the shape parameter. Alternative Generators: .. class:: SystemRandom([seed]) Class that uses the :func:`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 :meth:`seed` and :meth:`jumpahead` methods have no effect and are ignored. The :meth:`getstate` and :meth:`setstate` methods raise :exc:`NotImplementedError` if called. Examples of basic usage:: >>> random.random() # Random float x, 0.0 <= x < 1.0 0.37444887175646646 >>> random.uniform(1, 10) # Random float x, 1.0 <= x < 10.0 1.1800146073117523 >>> random.randint(1, 10) # Integer from 1 to 10, endpoints included 7 >>> random.randrange(0, 101, 2) # Even integer from 0 to 100 26 >>> random.choice('abcdefghij') # Choose a random element 'c' >>> items = [1, 2, 3, 4, 5, 6, 7] >>> random.shuffle(items) >>> items [7, 3, 2, 5, 6, 4, 1] >>> random.sample([1, 2, 3, 4, 5], 3) # Choose 3 elements [4, 1, 5] .. seealso:: 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.