The random module
generates pseudo-random numbers with various distributions. The
underlying uniform pseudo-random generator uses the Whichmann-Hill
algorithm, with a period of length 6,953,607,871,644. The resulting
pseudo-random numbers, while quite good, are not of cryptographic
quality. If you want physically generated random numbers rather than
algorithmically generated pseudo-random numbers, you may use
/dev/random or /dev/urandom
on platforms that support such pseudo-devices (such as recent Linux
releases). For an alternative, see http://www.fourmilab.ch/hotbits.
All functions
of module random are methods of a hidden instance
of class random.Random. You can instantiate
Random explicitly to get multiple generators that
do not share state. Explicit instantiation is advisable if you
require random numbers in multiple threads (threads are covered in
Chapter 14). This section documents the most
frequently used functions exposed by module
random.
Returns a random item from non-empty sequence
seq.
Returns an object S that represents the
current state of the generator. You can later pass
S to function setstate
in order to restore the generator's state.
Advances the generator state as if n
random numbers had been generated. Computing the new state is faster
than generating n random numbers would be.
Returns a random floating-point number r
from a uniform distribution, such that
0<=r<1.
randrange([start,]stop[,step])
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Like
choice(range(start,stop,step)),
but faster, since randrange does not need to build
the list that range would create.
Initializes the generator state. x can be
any hashable object. When x is
None, and also automatically when module
random is first loaded, seed
uses the current system time to get a seed.
x is normally a long integer up to
27814431486575L. Larger
x values are accepted, but may produce the
same generator states as smaller ones.
Restores the generator state. S must be
the result of a previous call to getstate.
Shuffles, in place, mutable sequence alist.
Returns a random floating-point number r
from a uniform distribution, such that
a<=r<b.
Module random also supplies functions that
generate pseudo-random floating-point numbers from many other
probability distributions (Beta, Gamma, exponential, Gauss, Pareto,
etc.). All of these functions internally call
random.random as their source of
randomness.