Random Generator#

The Generator provides access to a wide range of distributions, and served as a replacement for RandomState. The main difference between the two is that Generator relies on an additional BitGenerator to manage state and generate the random bits, which are then transformed into random values from useful distributions. The default BitGenerator used by Generator is PCG64. The BitGenerator can be changed by passing an instantized BitGenerator to Generator.

numpy.random.default_rng(seed=None)#

Construct a new Generator with the default BitGenerator (PCG64).

Parameters:

seed{None, int, array_like[ints], SeedSequence, BitGenerator, Generator, RandomState}, optional

A seed to initialize the BitGenerator. If None, then fresh, unpredictable entropy will be pulled from the OS. If an int or array_like[ints] is passed, then all values must be non-negative and will be passed to SeedSequence to derive the initial BitGenerator state. One may also pass in a SeedSequence instance. Additionally, when passed a BitGenerator, it will be wrapped by Generator. If passed a Generator, it will be returned unaltered. When passed a legacy RandomState instance it will be coerced to a Generator.

Returns:

Generator

The initialized generator object.

Notes

If seed is not a BitGenerator or a Generator, a new BitGenerator is instantiated. This function does not manage a default global instance.

See Seeding and entropy for more information about seeding.

Examples

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default_rng is the recommended constructor for the random number class Generator. Here are several ways we can construct a random number generator using default_rng and the Generator class.

Here we use default_rng to generate a random float:

>>> import numpy as np
>>> rng = np.random.default_rng(12345)
>>> print(rng)
Generator(PCG64)
>>> rfloat = rng.random()
>>> rfloat
0.22733602246716966
>>> type(rfloat)
<class 'float'>

Here we use default_rng to generate 3 random integers between 0 (inclusive) and 10 (exclusive):

>>> import numpy as np
>>> rng = np.random.default_rng(12345)
>>> rints = rng.integers(low=0, high=10, size=3)
>>> rints
array([6, 2, 7])
>>> type(rints[0])
<class 'numpy.int64'>

Here we specify a seed so that we have reproducible results:

>>> import numpy as np
>>> rng = np.random.default_rng(seed=42)
>>> print(rng)
Generator(PCG64)
>>> arr1 = rng.random((3, 3))
>>> arr1
array([[0.77395605, 0.43887844, 0.85859792],
       [0.69736803, 0.09417735, 0.97562235],
       [0.7611397 , 0.78606431, 0.12811363]])

If we exit and restart our Python interpreter, well see that we generate the same random numbers again:

>>> import numpy as np
>>> rng = np.random.default_rng(seed=42)
>>> arr2 = rng.random((3, 3))
>>> arr2
array([[0.77395605, 0.43887844, 0.85859792],
       [0.69736803, 0.09417735, 0.97562235],
       [0.7611397 , 0.78606431, 0.12811363]])

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class numpy.random.Generator(bit_generator)#

Container for the BitGenerators.

Generator exposes a number of methods for generating random numbers drawn from a variety of probability distributions. In addition to the distribution-specific arguments, each method takes a keyword argument size that defaults to None. If size is None, then a single value is generated and returned. If size is an integer, then a 1-D array filled with generated values is returned. If size is a tuple, then an array with that shape is filled and returned.

The function numpy.random.default_rng will instantiate a Generator with numpys default BitGenerator.

No Compatibility Guarantee

Generator does not provide a version compatibility guarantee. In particular, as better algorithms evolve the bit stream may change.

Parameters:

bit_generatorBitGenerator

BitGenerator to use as the core generator.

See also

default_rng

Recommended constructor for Generator.

Notes

The Python stdlib module random contains pseudo-random number generator with a number of methods that are similar to the ones available in Generator. It uses Mersenne Twister, and this bit generator can be accessed using MT19937. Generator, besides being NumPy-aware, has the advantage that it provides a much larger number of probability distributions to choose from.

Examples

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>>> from numpy.random import Generator, PCG64
>>> rng = Generator(PCG64())
>>> rng.standard_normal()
-0.203  # random

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Accessing the BitGenerator and spawning#

Simple random data#

Permutations#

The methods for randomly permuting a sequence are

The following table summarizes the behaviors of the methods.

The following subsections provide more details about the differences.

In-place vs. copy#

The main difference between Generator.shuffle and Generator.permutation is that Generator.shuffle operates in-place, while Generator.permutation returns a copy.

By default, Generator.permuted returns a copy. To operate in-place with Generator.permuted, pass the same array as the first argument and as the value of the out parameter. For example,

>>> import numpy as np
>>> rng = np.random.default_rng()
>>> x = np.arange(0, 15).reshape(3, 5)
>>> x 
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14]])
>>> y = rng.permuted(x, axis=1, out=x)
>>> x 
array([[ 1,  0,  2,  4,  3],  # random
       [ 6,  7,  8,  9,  5],
       [10, 14, 11, 13, 12]])

>>> y is x
True

Handling the axis parameter#

An important distinction for these methods is how they handle the axis parameter. Both Generator.shuffle and Generator.permutation treat the input as a one-dimensional sequence, and the axis parameter determines which dimension of the input array to use as the sequence. In the case of a two-dimensional array, axis=0 will, in effect, rearrange the rows of the array, and axis=1 will rearrange the columns. For example

>>> import numpy as np
>>> rng = np.random.default_rng()
>>> x = np.arange(0, 15).reshape(3, 5)
>>> x
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14]])
>>> rng.permutation(x, axis=1) 
array([[ 1,  3,  2,  0,  4],  # random
       [ 6,  8,  7,  5,  9],
       [11, 13, 12, 10, 14]])

Note that the columns have been rearranged in bulk: the values within each column have not changed.

The method Generator.permuted treats the axis parameter similar to how numpy.sort treats it. Each slice along the given axis is shuffled independently of the others. Compare the following example of the use of Generator.permuted to the above example of Generator.permutation:

>>> import numpy as np
>>> rng = np.random.default_rng()
>>> rng.permuted(x, axis=1) 
array([[ 1,  0,  2,  4,  3],  # random
       [ 5,  7,  6,  9,  8],
       [10, 14, 12, 13, 11]])

In this example, the values within each row (i.e. the values along axis=1) have been shuffled independently. This is not a bulk shuffle of the columns.

Shuffling non-NumPy sequences#

Generator.shuffle works on non-NumPy sequences. That is, if it is given a sequence that is not a NumPy array, it shuffles that sequence in-place.

>>> import numpy as np
>>> rng = np.random.default_rng()
>>> a = ['A', 'B', 'C', 'D', 'E']
>>> rng.shuffle(a)  # shuffle the list in-place
>>> a 
['B', 'D', 'A', 'E', 'C']  # random

Distributions#