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Generate cryptographically strong pseudo-random numbers suitable for managing secrets such as account authentication, tokens, and similar. See PEP 506 for more information. https://www.python.org/dev/peps/pep-0506/
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).
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.
getrandbits(self, k) getrandbits(k) -> x. Generates an int with k random bits.
_notimplemented(self, *args, **kwds) Method should not be called for a system random number generator.
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.
random(self) Get the next random number in the range [0.0, 1.0).
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, *args, **kwds) Stub method. Not used for a system random number generator.
_notimplemented(self, *args, **kwds) Method should not be called for a system random number generator.
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.
VERSION = 3
choice(seq) Choose a random element from a non-empty sequence.
compare_digest(a, b, /) Return 'a == b'. This function uses an approach designed to prevent timing analysis, making it appropriate for cryptography. a and b must both be of the same type: either str (ASCII only), or any bytes-like object. Note: If a and b are of different lengths, or if an error occurs, a timing attack could theoretically reveal information about the types and lengths of a and b--but not their values.
randbelow(exclusive_upper_bound) Return a random int in the range [0, n).
getrandbits(k) getrandbits(k) -> x. Generates an int with k random bits.
token_bytes(nbytes=None)
Return a random byte string containing *nbytes* bytes.
If *nbytes* is ``None`` or not supplied, a reasonable
default is used.
>>> token_bytes(16) #doctest:+SKIP
b'\xebr\x17D*t\xae\xd4\xe3S\xb6\xe2\xebP1\x8b'
token_hex(nbytes=None)
Return a random text string, in hexadecimal.
The string has *nbytes* random bytes, each byte converted to two
hex digits. If *nbytes* is ``None`` or not supplied, a reasonable
default is used.
>>> token_hex(16) #doctest:+SKIP
'f9bf78b9a18ce6d46a0cd2b0b86df9da'
token_urlsafe(nbytes=None)
Return a random URL-safe text string, in Base64 encoding.
The string has *nbytes* random bytes. If *nbytes* is ``None``
or not supplied, a reasonable default is used.
>>> token_urlsafe(16) #doctest:+SKIP
'Drmhze6EPcv0fN_81Bj-nA'
DEFAULT_ENTROPY = 32