1. 基于random模块

random.sample(population, k)

从 population 中不放回抽取 k 个元素

population：可以为可迭代是的数据对象，如list，set

k：随机抽取的样本数量

不放回抽样

import random
N = range(10)
m = 3
a = random.sample(N, m)
print(a)
# 从0~9的序列中采样了3个样本
Out: [0, 5, 8]

2. 基于numpy模块

numpy.random.choice(a, size=None, replace=True, p=None)

Help on built-in function choice:
choice(...) method of numpy.random.mtrand.RandomState instancechoice(a, size=None, replace=True, p=None)Generates a random sample from a given 1-D array.. versionadded:: 1.7.0.. note::New code should use the ``choice`` method of a ``default_rng()``instance instead; see `random-quick-start`.Parameters----------a : 1-D array-like or intIf an ndarray, a random sample is generated from its elements.If an int, the random sample is generated as if a were np.arange(a)size : int or tuple of ints, optionalOutput shape.  If the given shape is, e.g., ``(m, n, k)``, then``m * n * k`` samples are drawn.  Default is None, in which case asingle value is returned.replace : boolean, optionalWhether the sample is with or without replacementp : 1-D array-like, optionalThe probabilities associated with each entry in a.If not given the sample assumes a uniform distribution over allentries in a.Returns-------samples : single item or ndarrayThe generated random samplesRaises------ValueErrorIf a is an int and less than zero, if a or p are not 1-dimensional,if a is an array-like of size 0, if p is not a vector ofprobabilities, if a and p have different lengths, or ifreplace=False and the sample size is greater than the populationsizeSee Also--------randint, shuffle, permutationGenerator.choice: which should be used in new codeNotes-----Sampling random rows from a 2-D array is not possible with this function,but is possible with `Generator.choice` through its ``axis`` keyword.Examples--------Generate a uniform random sample from np.arange(5) of size 3:>>> np.random.choice(5, 3)array([0, 3, 4]) # random>>> #This is equivalent to np.random.randint(0,5,3)Generate a non-uniform random sample from np.arange(5) of size 3:>>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])array([3, 3, 0]) # randomGenerate a uniform random sample from np.arange(5) of size 3 withoutreplacement:>>> np.random.choice(5, 3, replace=False)array([3,1,0]) # random>>> #This is equivalent to np.random.permutation(np.arange(5))[:3]Generate a non-uniform random sample from np.arange(5) of size3 without replacement:>>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])array([2, 3, 0]) # randomAny of the above can be repeated with an arbitrary array-likeinstead of just integers. For instance:>>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']>>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'], # randomdtype='<U11')

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[Python] 随机抽样

1. 基于random模块

2. 基于numpy模块

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