[Python] 随机抽样

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 instance
    choice(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 int
        If 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, optional
        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
        ``m * n * k`` samples are drawn.  Default is None, in which case a
        single value is returned.
    replace : boolean, optional
        Whether the sample is with or without replacement
    p : 1-D array-like, optional
        The probabilities associated with each entry in a.
        If not given the sample assumes a uniform distribution over all
        entries in a.
    
    Returns
    -------
    samples : single item or ndarray
        The generated random samples
    
    Raises
    ------
    ValueError
        If 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 of
        probabilities, if a and p have different lengths, or if
        replace=False and the sample size is greater than the population
        size
    
    See Also
    --------
    randint, shuffle, permutation
    Generator.choice: which should be used in new code
    
    Notes
    -----
    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]) # random
    
    Generate a uniform random sample from np.arange(5) of size 3 without
    replacement:
    
    >>> 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 size
    3 without replacement:
    
    >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
    array([2, 3, 0]) # random
    
    Any of the above can be repeated with an arbitrary array-like
    instead 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'], # random
          dtype=')