Random sampling (numpy.random
)¶
Numpy’s random number routines produce pseudo random numbers using
combinations of a BitGenerator to create sequences and a Generator
to use those sequences to sample from different statistical distributions:
 BitGenerators: Objects that generate random numbers. These are typically unsigned integer words filled with sequences of either 32 or 64 random bits.
 Generators: Objects that transform sequences of random bits from a BitGenerator into sequences of numbers that follow a specific probability distribution (such as uniform, Normal or Binomial) within a specified interval.
Since Numpy version 1.17.0 the Generator can be initialized with a
number of different BitGenerators. It exposes many different probability
distributions. See NEP 19 for context on the updated random Numpy number
routines. The legacy RandomState
random number routines are still
available, but limited to a single BitGenerator.
For convenience and backward compatibility, a single RandomState
instance’s methods are imported into the numpy.random namespace, see
Legacy Random Generation for the complete list.
Quick Start¶
By default, Generator
uses bits provided by PCG64
which
has better statistical properties than the legacy mt19937 random
number generator in RandomState
.
# Uses the old numpy.random.RandomState
from numpy import random
random.standard_normal()
Generator
can be used as a replacement for RandomState
. Both class
instances now hold a internal BitGenerator instance to provide the bit
stream, it is accessible as gen.bit_generator
. Some longoverdue API
cleanup means that legacy and compatibility methods have been removed from
Generator
RandomState 
Generator 
Notes 
random_sample , 
random 
Compatible with random.random 
rand 

randint , 
integers 
Add an endpoint kwarg 
random_integers 

tomaxint 
removed 

seed 
removed  Use spawn 
See newordifferent for more information
# As replacement for RandomState(); default_rng() instantiates Generator with
# the default PCG64 BitGenerator.
from numpy.random import default_rng
rg = default_rng()
rg.standard_normal()
rg.bit_generator
Something like the following code can be used to support both RandomState
and Generator
, with the understanding that the interfaces are slightly
different
try:
rg_integers = rg.integers
except AttributeError:
rg_integers = rg.randint
a = rg_integers(1000)
Seeds can be passed to any of the BitGenerators. The provided value is mixed
via SeedSequence
to spread a possible sequence of seeds across a wider
range of initialization states for the BitGenerator. Here PCG64
is used and
is wrapped with a Generator
.
from numpy.random import Generator, PCG64
rg = Generator(PCG64(12345))
rg.standard_normal()
Introduction¶
The new infrastructure takes a different approach to producing random numbers
from the RandomState
object. Random number generation is separated into
two components, a bit generator and a random generator.
The BitGenerator has a limited set of responsibilities. It manages state and provides functions to produce random doubles and random unsigned 32 and 64bit values.
The random generator
takes the
bit generatorprovided stream and transforms them into more useful
distributions, e.g., simulated normal random values. This structure allows
alternative bit generators to be used with little code duplication.
The Generator
is the userfacing object that is nearly identical to
RandomState
. The canonical method to initialize a generator passes a
PCG64
bit generator as the sole argument.
from numpy.random import default_rng
rg = default_rng(12345)
rg.random()
One can also instantiate Generator
directly with a BitGenerator instance.
To use the older MT19937
algorithm, one can instantiate it directly
and pass it to Generator
.
from numpy.random import Generator, MT19937
rg = Generator(MT19937(12345))
rg.random()
What’s New or Different¶
Warning
The BoxMuller method used to produce NumPy’s normals is no longer available
in Generator
. It is not possible to reproduce the exact random
values using Generator for the normal distribution or any other
distribution that relies on the normal such as the RandomState.gamma
or
RandomState.standard_t
. If you require bitwise backward compatible
streams, use RandomState
.
 The Generator’s normal, exponential and gamma functions use 256step Ziggurat methods which are 210 times faster than NumPy’s BoxMuller or inverse CDF implementations.
 Optional
dtype
argument that acceptsnp.float32
ornp.float64
to produce either single or double prevision uniform random variables for select distributions  Optional
out
argument that allows existing arrays to be filled for select distributions random_entropy
provides access to the system source of randomness that is used in cryptographic applications (e.g.,/dev/urandom
on Unix). All BitGenerators can produce doubles, uint64s and uint32s via CTypes
(
ctypes
) and CFFI (cffi
). This allows the bit generators to be used in numba.  The bit generators can be used in downstream projects via Cython.
integers
is now the canonical way to generate integer random numbers from a discrete uniform distribution. Therand
andrandn
methods are only available through the legacyRandomState
. Theendpoint
keyword can be used to specify open or closed intervals. This replaces bothrandint
and the deprecatedrandom_integers
.random
is now the canonical way to generate floatingpoint random numbers, which replacesRandomState.random_sample
, RandomState.sample, and RandomState.ranf. This is consistent with Python’srandom.random
. All BitGenerators in numpy use
SeedSequence
to convert seeds into initialized states.
See What’s New or Different for a complete list of improvements and
differences from the traditional Randomstate
.
Parallel Generation¶
The included generators can be used in parallel, distributed applications in one of three ways:
Features¶
Original Source¶
This package was developed independently of NumPy and was integrated in version 1.17.0. The original repo is at https://github.com/bashtage/randomgen.