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?
The first block of code uses a numpy.random.* function. numpy.random.* functions (including numpy.random.binomial) make use of a global RandomState object which is shared across the application.
The second block of code creates a pseudorandom generator object with default_rng() and uses that object to generate pseudorandom 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 pseudorandom 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.
numpy.random.* functions (including numpy.random.binomial) are obsolete. Rather, in most cases, they should be avoided whenever reproducible "randomness" is desired, with unit tests being a possible exception. See the RNG Policy for more information.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.
