如何从 Python 中的总体生成随机样本？

Question

我正在尝试解决这个问题：

Generate 1,000 random samples of size 50 from population. Calculate the mean of each of these samples (so you should have 1,000 means) and put them in a list norm_samples_50.

我的猜测是我必须使用 randn 函数，但我不能完全猜测如何根据上述问题形成语法。我已完成研究，但找不到合适的答案。

Answer 1

这是你想要的吗？

import random

# Creating a population replace with your own: 
population = [random.randint(0, 1000) for x in range(1000)]

# Creating the list to store all the means of each sample: 
means = []

for x in range(1000):
    # Creating a random sample of the population with size 50: 
    sample = random.sample(population,50)
    # Getting the sum of values in the sample then dividing by 50: 
    mean = sum(sample)/50
    # Adding this mean to the list of means
    means.append(mean)

Answer 2

使用 Numpy 的非常有效的解决方案。

import numpy


sample_list = []

for i in range(50): # 50 times - we generate a 1000 of 0-1000random - 
    rand_list = numpy.random.randint(0,1000, 1000)
    # generates a list of 1000 elements with values 0-1000
    sample_list.append(sum(rand_list)/50) # sum all elements

Python一行

from numpy.random import randint


sample_list = [sum(randint(0,1000,1000))/50 for _ in range(50)]

为什么要使用 Numpy？它非常高效且非常准确（十进制）。这个库就是为这些类型的计算和数字而创建的。使用标准库中的 random 很好，但速度和可靠性都不尽如人意。

如何从 Python 中的总体生成随机样本？

How to generate random samples from a population in Python?

python

random

sample