This rule raises an issue when legacy numpy.random.RandomState is used.

Why is this an issue?

Using a predictable seed is a common best practice when using NumPy to create reproducible results. To that end, using np.random.seed(number) to set the seed of the global numpy.random.RandomState has been the privileged solution for a long time.

numpy.random.RandomState and its associated methods rely on a global state, which may be problematic when threads or other forms of concurrency are involved. The global state may be altered and the global seed may be reset at various points in the program (for instance, through an imported package or script), which would lead to irreproducible results.

Instead, the preferred best practice to generate reproducible pseudorandom numbers is to instantiate a numpy.random.Generator object with a seed and reuse it in different parts of the code. This avoids the reliance on a global state. Whenever a new seed is needed, a new generator may be created instead of mutating a global state.

Below is the list of legacy functions and their alternatives:

Legacy function name

numpy.random.Generator alternative

numpy.random.RandomState.seed

numpy.random.default_rng

numpy.random.RandomState.rand

numpy.random.Generator.random

numpy.random.RandomState.randn

numpy.random.Generator.standard_normal

numpy.random.RandomState.randint

numpy.random.Generator.integers

numpy.random.RandomState.random_integers

numpy.random.Generator.integers

numpy.random.RandomState.random_sample

numpy.random.Generator.random

numpy.random.RandomState.choice

numpy.random.Generator.choice

numpy.random.RandomState.bytes

numpy.random.Generator.bytes

numpy.random.RandomState.shuffle

numpy.random.Generator.shuffle

numpy.random.RandomState.permutation

numpy.random.Generator.permutation

numpy.random.RandomState.beta

numpy.random.Generator.beta

numpy.random.RandomState.binomial

numpy.random.Generator.binomial

numpy.random.RandomState.chisquare

numpy.random.Generator.chisquare

numpy.random.RandomState.dirichlet

numpy.random.Generator.dirichlet

numpy.random.RandomState.exponential

numpy.random.Generator.exponential

numpy.random.RandomState.f

numpy.random.Generator.f

numpy.random.RandomState.gamma

numpy.random.Generator.gamma

numpy.random.RandomState.geometric

numpy.random.Generator.geometric

numpy.random.RandomState.gumbel

numpy.random.Generator.gumbel

numpy.random.RandomState.hypergeometric

numpy.random.Generator.hypergeometric

numpy.random.RandomState.laplace

numpy.random.Generator.laplace

numpy.random.RandomState.logistic

numpy.random.Generator.logistic

numpy.random.RandomState.lognormal

numpy.random.Generator.lognormal

numpy.random.RandomState.logseries

numpy.random.Generator.logseries

numpy.random.RandomState.multinomial

numpy.random.Generator.multinomial

numpy.random.RandomState.multivariate_normal

numpy.random.Generator.multivariate_normal

numpy.random.RandomState.negative_binomial

numpy.random.Generator.negative_binomial

numpy.random.RandomState.noncentral_chisquare

numpy.random.Generator.noncentral_chisquare

numpy.random.RandomState.noncentral_f

numpy.random.Generator.noncentral_f

numpy.random.RandomState.normal

numpy.random.Generator.normal

numpy.random.RandomState.pareto

numpy.random.Generator.pareto

numpy.random.RandomState.poisson

numpy.random.Generator.poisson

numpy.random.RandomState.power

numpy.random.Generator.power

numpy.random.RandomState.rayleigh

numpy.random.Generator.rayleigh

numpy.random.RandomState.standard_cauchy

numpy.random.Generator.standard_cauchy

numpy.random.RandomState.standard_exponential

numpy.random.Generator.standard_exponential

numpy.random.RandomState.standard_gamma

numpy.random.Generator.standard_gamma

numpy.random.RandomState.standard_normal

numpy.random.Generator.standard_normal

numpy.random.RandomState.standard_t

numpy.random.Generator.standard_t

numpy.random.RandomState.triangular

numpy.random.Generator.triangular

numpy.random.RandomState.uniform

numpy.random.Generator.uniform

numpy.random.RandomState.vonmises

numpy.random.Generator.vonmises

numpy.random.RandomState.wald

numpy.random.Generator.wald

numpy.random.RandomState.weibull

numpy.random.Generator.weibull

numpy.random.RandomState.zipf

numpy.random.Generator.zipf

numpy.random.beta

numpy.random.Generator.beta

numpy.random.binomial

numpy.random.Generator.binomial

numpy.random.bytes

numpy.random.Generator.bytes

numpy.random.chisquare

numpy.random.Generator.chisquare

numpy.random.choice

numpy.random.Generator.choice

numpy.random.dirichlet

numpy.random.Generator.dirichlet

numpy.random.exponential

numpy.random.Generator.exponential

numpy.random.f

numpy.random.Generator.f

numpy.random.gamma

numpy.random.Generator.gamma

numpy.random.geometric

numpy.random.Generator.geometric

numpy.random.gumbel

numpy.random.Generator.gumbel

numpy.random.hypergeometric

numpy.random.Generator.hypergeometric

numpy.random.laplace

numpy.random.Generator.laplace

numpy.random.logistic

numpy.random.Generator.logistic

numpy.random.lognormal

numpy.random.Generator.lognormal

numpy.random.logseries

numpy.random.Generator.logseries

numpy.random.multinomial

numpy.random.Generator.multinomial

numpy.random.multivariate_normal

numpy.random.Generator.multivariate_normal

numpy.random.negative_binomial

numpy.random.Generator.negative_binomial

numpy.random.noncentral_chisquare

numpy.random.Generator.noncentral_chisquare

numpy.random.noncentral_f

numpy.random.Generator.noncentral_f

numpy.random.normal

numpy.random.Generator.normal

numpy.random.pareto

numpy.random.Generator.pareto

numpy.random.permutation

numpy.random.Generator.permutation

numpy.random.poisson

numpy.random.Generator.poisson

numpy.random.power

numpy.random.Generator.power

numpy.random.rand

numpy.random.Generator.random

numpy.random.randint

numpy.random.Generator.integers

numpy.random.randn

numpy.random.Generator.standard_normal

numpy.random.random

numpy.random.Generator.random

numpy.random.random_integers

numpy.random.Generator.integers

numpy.random.random_sample

numpy.random.Generator.random

numpy.random.ranf

numpy.random.Generator.random

numpy.random.rayleigh

numpy.random.Generator.rayleigh

numpy.random.sample

numpy.random.Generator.random

numpy.random.seed

numpy.random.default_rng

numpy.random.shuffle

numpy.random.Generator.shuffle

numpy.random.standard_cauchy

numpy.random.Generator.standard_cauchy

numpy.random.standard_exponential

numpy.random.Generator.standard_exponential

numpy.random.standard_gamma

numpy.random.Generator.standard_gamma

numpy.random.standard_normal

numpy.random.Generator.standard_normal

numpy.random.standard_t

numpy.random.Generator.standard_t

numpy.random.triangular

numpy.random.Generator.triangular

numpy.random.uniform

numpy.random.Generator.uniform

numpy.random.vonmises

numpy.random.Generator.vonmises

numpy.random.wald

numpy.random.Generator.wald

numpy.random.weibull

numpy.random.Generator.weibull

numpy.random.zipf

numpy.random.Generator.zipf

How to fix it

To fix this issue, replace usages of numpy.random.RandomState to numpy.random.Generator.

Code examples

Noncompliant code example

import numpy as np
np.random.seed(42)
x = np.random.randn()  # Noncompliant: this relies on numpy.random.RandomState, which is deprecated

Compliant solution

import numpy as np
generator = np.random.default_rng(42)
x = generator.standard_normal()

Resources

Documentation