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Random numbers the non-legacy way

{:.no_toc}

* TOC {:toc}

Goal

If you don't see something like np.random.default_rng() in your code then you are probably using the old Legacy Random Generation.

Don't use the legacy methods for new source code!!!

numpy.random.random() == old == bad == don't use​

Do it like this:

import numpy as np

rng = np.random.default_rng()
random_values = rng.random(size=(2, 10))

Questions to David Rotermund

Random Generator

Typical usage

import numpy as np

rng = np.random.default_rng()
random_values = rng.random(size=(2, 10))

print(random_values)

Output:

[[0.81103943 0.1110591  0.42978062 0.47818377 0.91138636 0.47051031
  0.08662082 0.1643707  0.48717037 0.17870536]
 [0.94499902 0.74089677 0.12221184 0.61603001 0.91198789 0.33900609
  0.75832792 0.74465679 0.19940125 0.56674595]]

With seed:

import numpy as np

rng = np.random.default_rng(seed=23)
random_values = rng.random(size=(2, 10))

print(random_values)

Output:

[[0.69393308 0.64145822 0.12864422 0.11370805 0.65334552 0.85345711
  0.20177913 0.21801864 0.71658464 0.47069967]
 [0.41522193 0.3491478  0.06385375 0.45466617 0.30145328 0.38907675
  0.54029782 0.68358969 0.62475238 0.74270445]]

Changing the random number generator

Default

import numpy as np

rng = np.random.default_rng()
print(rng)  # -> Generator(PCG64)

If you don't like it there are other options:
PCG64 -- The default A fast generator that can be advanced by an arbitrary amount. See the documentation for advance. PCG-64 has a period of 2^128. See the PCG author’s page for more details about this class of PRNG.​
MT19937 The standard Python BitGenerator. Adds a MT19937.jumped function that returns a new generator with state as-if 2^128 draws have been made.
PCG64DXSM An upgraded version of PCG-64 with better statistical properties in parallel contexts. See Upgrading PCG64 with PCG64DXSM for more information on these improvements.​
Philox A counter-based generator capable of being advanced an arbitrary number of steps or generating independent streams. See the Random123 page for more details about this class of bit generators.​
SFC64 A fast generator based on random invertible mappings. Usually the fastest generator of the four. See the SFC author’s page for (a little) more detail.​

Distributions (you will use)

The most important ones are in bold. If you see a function argument out, then you can reuse an existing np array (i.e. in-place operation) as target.
integers(low[, high, size, dtype, endpoint]) Return random integers from low (inclusive) to high (exclusive), or if endpoint=True, low (inclusive) to high (inclusive).
random([size, dtype, out]) Return random floats in the half-open interval [0.0, 1.0).
choice(a[, size, replace, p, axis, shuffle]) Generates a random sample from a given array
bytes(length) Return random bytes.
binomial(n, p[, size]) Draw samples from a binomial distribution.
multinomial(n, pvals[, size]) Draw samples from a multinomial distribution.
multivariate_normal(mean, cov[, size, ...]) Draw random samples from a multivariate normal distribution.
normal([loc, scale, size]) Draw random samples from a normal (Gaussian) distribution.
poisson([lam, size]) Draw samples from a Poisson distribution.
standard_normal([size, dtype, out]) Draw samples from a standard Normal distribution (mean=0, stdev=1).
uniform([low, high, size]) Draw samples from a uniform distribution.

random

import numpy as np

rng = np.random.default_rng()
random_values = rng.random(size=(2, 10))
print(random_values)

Output:

[[0.75309105 0.15751286 0.49454759 0.18204807 0.88459006 0.78685769
  0.68525047 0.4000365  0.45317167 0.62412358]
 [0.01082224 0.13257961 0.75638974 0.84886965 0.19755022 0.18697649
  0.47064409 0.66128207 0.30285691 0.53465021]]

integers

import numpy as np

rng = np.random.default_rng()
random_values = rng.integers(
    low=1, high=3, size=(2, 10), dtype=np.uint64, endpoint=True
)
print(random_values)

Output:

[[2 3 3 2 1 3 1 1 2 2]
 [3 3 2 3 3 2 3 3 1 3]]

choice

import numpy as np

rng = np.random.default_rng()
p = np.array([1, 2, 3]).astype(np.float64)
p /= p.sum()
print(f"p: {p}")
random_values = rng.choice(a=p.shape[0], p=p, size=(2, 10))
print(random_values)

Output:

p: [0.16666667 0.33333333 0.5       ]
[[0 2 2 1 2 1 2 1 0 1]
 [2 0 1 2 2 1 0 2 1 2]]

Permutations
shuffle(x[, axis]) Modify an array or sequence in-place by shuffling its contents.
permutation(x[, axis]) Randomly permute a sequence, or return a permuted range.
permuted(x[, axis, out]) Randomly permute x along axis axis.
method copy/in-place axis handling
shuffle in-place as if 1d
permutation copy as if 1d
permuted either (use ‘out’ for in-place) axis independent

shuffle

import numpy as np

rng = np.random.default_rng()
idx_randomized = np.arange(0, 10)
rng.shuffle(idx_randomized)

print(idx_randomized)

Output:

[0 2 8 9 5 4 3 6 1 7]

permutation

import numpy as np

rng = np.random.default_rng()
idx_randomized = rng.permutation(10)

print(idx_randomized)

Output:

[9 4 7 2 6 3 1 8 5 0]

permuted

import numpy as np

rng = np.random.default_rng()
idx = np.arange(0, 10)

idx_randomized = rng.permuted(idx)

print(idx_randomized)

Output:

[4 1 2 8 9 6 0 5 7 3]

All Distributions

You need more distributions? Go here.

Multithreaded Generation

The four core distribution (random, standard_normal, standard_exponential, and standard_gamma) can be used with multi-threading. Please look here for an example.