How to use numpy.random to generate random numbers from a certain distribution?

问题

I am somewhat confused about how to use numpy.random to generate random values from a give distribution, say, binomial. I thought it would be

import numpy as np
np.random.binomial(10, 0.3, 5)

However, NumPy reference page shows something like

from numpy.random import default_rng
rg = default_rng()
rg.binomial(10, 0.3, 5)

Both seem to be working well. Which one is the correct or better way? What is the difference if there is any?

回答1:

The first block of code uses a numpy.random.* function. numpy.random.* functions (including numpy.random.binomial) make use of a global random generator object which is shared across the application.

The second block of code creates a random generator object with default_rng() and uses that object to generate random numbers without relying on global state.

Note that numpy.random.binomial (in addition to other numpy.random.* functions) is now a legacy function as of NumPy 1.17; NumPy 1.17 introduces a new random number generation system, which is demonstrated in the second block of code in your question. It was the result of a proposal to change the RNG policy. The desire to avoid global state was one of the reasons for the change in this policy.

回答2:

This does not work in Python2.7 / numpy 1.16 :

>>> from numpy.random import default_rng
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ImportError: cannot import name default_rng

回答3:

import random
random.choice([2,44,55,66])

A crucial thing to understand about the random choice method is that Python doesn't care about the fundamental nature of the objects that are contained in that list.

来源：https://stackoverflow.com/questions/59728162/how-to-use-numpy-random-to-generate-random-numbers-from-a-certain-distribution

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python

numpy

random