
``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.

Bookkeeping functions:

random.seed([x], version=2)

   Initialize the random number generator.

   If *x* 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 *x* 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, the ``hash()`` of *x* is used
   instead.

   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 ``setstate()`` 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.

Functions for integers:

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)``.

Functions for sequences:

random.choice(seq)

   Return a random element from the non-empty sequence *seq*. If *seq*
   is empty, raises ``IndexError``.

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()``.

   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.

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 an ``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.

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``.

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.

Alternative Generator:

class class 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.

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.


Notes on Reproducibility
========================

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:

* If a new seeding method is added, then a backward compatible seeder
  will be offered.

* The generator's ``random()`` method will continue to produce the
  same sequence when the compatible seeder is given the same seed.


Examples and Recipes
====================

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.randrange(10)                 # Integer from 0 to 9
   7

   >>> random.randrange(0, 101, 2)          # Even integer from 0 to 100
   26

   >>> random.choice('abcdefghij')          # Single 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)   # Three samples without replacement
   [4, 1, 5]

A common task is to make a ``random.choice()`` with weighted
probababilites.

If the weights are small integer ratios, a simple technique is to
build a sample population with repeats:

   >>> weighted_choices = [('Red', 3), ('Blue', 2), ('Yellow', 1), ('Green', 4)]
   >>> population = [val for val, cnt in weighted_choices for i in range(cnt)]
   >>> random.choice(population)
   'Green'

A more general approach is to arrange the weights in a cumulative
distribution with ``itertools.accumulate()``, and then locate the
random value with ``bisect.bisect()``:

   >>> choices, weights = zip(*weighted_choices)
   >>> cumdist = list(itertools.accumulate(weights))
   >>> x = random.random() * cumdist[-1]
   >>> choices[bisect.bisect(cumdist, x)]
   'Blue'
