如何使用 numpy.random 从某个分布中生成随机数？

Question

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?

Answer 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.

Answer 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

Answer 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.