Random sampling (
numpy.random module implements pseudo-random number generators
(PRNGs or RNGs, for short) with the ability to draw samples from a variety of
probability distributions. In general, users will create a
default_rng and call the various methods on it to obtain samples from
>>> import numpy as np >>> rng = np.random.default_rng() # Generate one random float uniformly distributed over the range [0, 1) >>> rng.random() 0.06369197489564249 # may vary # Generate an array of 10 numbers according to a unit Gaussian distribution. >>> rng.standard_normal(10) array([-0.31018314, -1.8922078 , -0.3628523 , -0.63526532, 0.43181166, # may vary 0.51640373, 1.25693945, 0.07779185, 0.84090247, -2.13406828]) # Generate an array of 5 integers uniformly over the range [0, 10). >>> rng.integers(low=0, high=10, size=5) array([8, 7, 6, 2, 0]) # may vary
Our RNGs are deterministic sequences and can be reproduced by specifying a seed integer to
derive its initial state. By default, with no seed provided,
default_rng will create
seed the RNG from nondeterministic data from the operating system and therefore
generate different numbers each time. The pseudo-random sequences will be
independent for all practical purposes, at least those purposes for which our
pseudo-randomness was good for in the first place.
>>> rng1 = np.random.default_rng() >>> rng1.random() 0.6596288841243357 # may vary >>> rng2 = np.random.default_rng() >>> rng2.random() 0.11885628817151628 # may vary
The pseudo-random number generators implemented in this module are designed
for statistical modeling and simulation. They are not suitable for security
or cryptographic purposes. See the
secrets module from the
standard library for such use cases.
Seeds should be large positive integers.
default_rng can take positive
integers of any size. We recommend using very large, unique numbers to ensure
that your seed is different from anyone else’s. This is good practice to ensure
that your results are statistically independent from theirs unless you are
intentionally trying to reproduce their result. A convenient way to get
such a seed number is to use
secrets.randbits to get an
arbitrary 128-bit integer.
>>> import secrets >>> import numpy as np >>> secrets.randbits(128) 122807528840384100672342137672332424406 # may vary >>> rng1 = np.random.default_rng(122807528840384100672342137672332424406) >>> rng1.random() 0.5363922081269535 >>> rng2 = np.random.default_rng(122807528840384100672342137672332424406) >>> rng2.random() 0.5363922081269535
Generator and its associated infrastructure was introduced in NumPy version
1.17.0. There is still a lot of code that uses the older
RandomState and the
numpy.random. While there are no plans to remove them at this
time, we do recommend transitioning to
Generator as you can. The algorithms
are faster, more flexible, and will receive more improvements in the future.
For the most part,
Generator can be used as a replacement for
See Legacy Random Generation for information on the legacy infrastructure,
What’s New or Different for information on transitioning, and NEP 19 for some of the reasoning for the transition.
Users primarily interact with
Generator instances. Each
BitGenerator instance that implements the core RNG algorithm. The
BitGenerator has a limited set of responsibilities. It manages state and
provides functions to produce random doubles and random unsigned 32- and 64-bit
Generator takes the bit generator-provided 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
NumPy implements several different
BitGenerator classes implementing
different RNG algorithms.
default_rng currently uses
PCG64 as the
BitGenerator. It has better statistical properties and performance
MT19937 algorithm used in the legacy
Bit Generators for more details on the supported BitGenerators.
default_rng and BitGenerators delegate the conversion of seeds into RNG
SeedSequence implements a sophisticated
algorithm that intermediates between the user’s input and the internal
implementation details of each
BitGenerator algorithm, each of which can
require different amounts of bits for its state. Importantly, it lets you use
arbitrary-sized integers and arbitrary sequences of such integers to mix
together into the RNG state. This is a useful primitive for constructing
a flexible pattern for parallel RNG streams.
For backward compatibility, we still maintain the legacy
It continues to use the
MT19937 algorithm by default, and old seeds continue
to reproduce the same results. The convenience Functions in numpy.random
are still aliases to the methods on a single global
RandomState instance. See
Legacy Random Generation for the complete details. See What’s New or Different for
a detailed comparison between
The included generators can be used in parallel, distributed applications in a number of ways:
Users with a very large amount of parallelism will want to consult Upgrading PCG64 with PCG64DXSM.
- Parallel Applications
- Multithreaded Generation
- What’s New or Different
- Comparing Performance
- C API for random
- Examples of using Numba, Cython, CFFI
Original Source of the Generator and BitGenerators#
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.