Legacy Random Generation¶
The RandomState
provides access to
legacy generators. This generator is considered frozen and will have
no further improvements. It is guaranteed to produce the same values
as the final point release of NumPy v1.16. These all depend on BoxMuller
normals or inverse CDF exponentials or gammas. This class should only be used
if it is essential to have randoms that are identical to what
would have been produced by previous versions of NumPy.
RandomState
adds additional information
to the state which is required when using BoxMuller normals since these
are produced in pairs. It is important to use
RandomState.get_state
, and not the underlying bit generators
state, when accessing the state so that these extra values are saved.
Although we provide the MT19937
BitGenerator for use independent of
RandomState
, note that its default seeding uses SeedSequence
rather than the legacy seeding algorithm. RandomState
will use the
legacy seeding algorithm. The methods to use the legacy seeding algorithm are
currently private as the main reason to use them is just to implement
RandomState
. However, one can reset the state of MT19937
using the state of the RandomState
:
from numpy.random import MT19937
from numpy.random import RandomState
rs = RandomState(12345)
mt19937 = MT19937()
mt19937.state = rs.get_state()
rs2 = RandomState(mt19937)
# Same output
rs.standard_normal()
rs2.standard_normal()
rs.random()
rs2.random()
rs.standard_exponential()
rs2.standard_exponential()

class
numpy.random.
RandomState
(seed=None)¶ Container for the slow Mersenne Twister pseudorandom number generator. Consider using a different BitGenerator with the Generator container instead.
RandomState
andGenerator
expose a number of methods for generating random numbers drawn from a variety of probability distributions. In addition to the distributionspecific arguments, each method takes a keyword argument size that defaults toNone
. If size isNone
, then a single value is generated and returned. If size is an integer, then a 1D array filled with generated values is returned. If size is a tuple, then an array with that shape is filled and returned.Compatibility Guarantee
A fixed bit generator using a fixed seed and a fixed series of calls to ‘RandomState’ methods using the same parameters will always produce the same results up to roundoff error except when the values were incorrect.
RandomState
is effectively frozen and will only receive updates that are required by changes in the the internals of Numpy. More substantial changes, including algorithmic improvements, are reserved forGenerator
. Parameters
 seed{None, int, array_like, BitGenerator}, optional
Random seed used to initialize the pseudorandom number generator or an instantized BitGenerator. If an integer or array, used as a seed for the MT19937 BitGenerator. Values can be any integer between 0 and 2**32  1 inclusive, an array (or other sequence) of such integers, or
None
(the default). Ifseed
isNone
, then theMT19937
BitGenerator is initialized by reading data from/dev/urandom
(or the Windows analogue) if available or seed from the clock otherwise.
See also
Notes
The Python stdlib module “random” also contains a Mersenne Twister pseudorandom number generator with a number of methods that are similar to the ones available in
RandomState
.RandomState
, besides being NumPyaware, has the advantage that it provides a much larger number of probability distributions to choose from.
Seeding and State¶
Return a tuple representing the internal state of the generator. 


Set the internal state of the generator from a tuple. 

Reseed a legacy MT19937 BitGenerator 
Simple random data¶

Random values in a given shape. 

Return a sample (or samples) from the “standard normal” distribution. 

Return random integers from low (inclusive) to high (exclusive). 

Random integers of type np.int_ between low and high, inclusive. 

Return random floats in the halfopen interval [0.0, 1.0). 

Generates a random sample from a given 1D array 

Return random bytes. 
Permutations¶

Modify a sequence inplace by shuffling its contents. 

Randomly permute a sequence, or return a permuted range. 
Distributions¶

Draw samples from a Beta distribution. 

Draw samples from a binomial distribution. 

Draw samples from a chisquare distribution. 

Draw samples from the Dirichlet distribution. 

Draw samples from an exponential distribution. 

Draw samples from an F distribution. 

Draw samples from a Gamma distribution. 

Draw samples from the geometric distribution. 

Draw samples from a Gumbel distribution. 

Draw samples from a Hypergeometric distribution. 

Draw samples from the Laplace or double exponential distribution with specified location (or mean) and scale (decay). 

Draw samples from a logistic distribution. 

Draw samples from a lognormal distribution. 

Draw samples from a logarithmic series distribution. 

Draw samples from a multinomial distribution. 

Draw random samples from a multivariate normal distribution. 

Draw samples from a negative binomial distribution. 

Draw samples from a noncentral chisquare distribution. 

Draw samples from the noncentral F distribution. 

Draw random samples from a normal (Gaussian) distribution. 

Draw samples from a Pareto II or Lomax distribution with specified shape. 

Draw samples from a Poisson distribution. 

Draws samples in [0, 1] from a power distribution with positive exponent a  1. 

Draw samples from a Rayleigh distribution. 

Draw samples from a standard Cauchy distribution with mode = 0. 

Draw samples from the standard exponential distribution. 

Draw samples from a standard Gamma distribution. 

Draw samples from a standard Normal distribution (mean=0, stdev=1). 

Draw samples from a standard Student’s t distribution with df degrees of freedom. 

Draw samples from the triangular distribution over the interval 

Draw samples from a uniform distribution. 

Draw samples from a von Mises distribution. 

Draw samples from a Wald, or inverse Gaussian, distribution. 

Draw samples from a Weibull distribution. 

Draw samples from a Zipf distribution. 