random.sample 從集合或者序列中，無放回采樣

用法：

sample_list=random.sample(population, k) # population : 可以是一個(gè)集合，也可以是序列（list,tuple，或者字符串） # k : 0<= k<= len(population) # 返回一個(gè)list，如果是字符串的話，返回的是['index1','index2']隨機(jī)選擇的索引值 # 注意：不會(huì)改變?cè)瓉淼?population

測(cè)試：

import randomrandom.seed(66) a1=list(range(0,10)) print(a1,type(a1)) b1= random.sample(a1,2) print(b1) print(a1) """ 結(jié)果： [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] <class 'list'> [1, 4] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] """a2=tuple(range(0,10)) print(a2,type(a2)) b2= random.sample(a2,2) print(b2) print(a2) """ 結(jié)果： (0, 1, 2, 3, 4, 5, 6, 7, 8, 9) <class 'tuple'> [6, 3] (0, 1, 2, 3, 4, 5, 6, 7, 8, 9) """a3='1234567890' print(a3,type(a3)) b3= random.sample(a3,2) print(b3) print(a3) """ 結(jié)果： 1234567890 <class 'str'> ['8', '5'] 1234567890 """a4=dict(spam = 1, egg = 2, bar =3) print(a4,type(a4)) b4= random.sample(a4,2) print(b4) print(a4) # 報(bào)錯(cuò)：不能處理dicta5 = set(("Google", "Runoob", "Taobao")) print(a5,type(a5)) b5= random.sample(a5,2) print(b5) print(a5) """ 結(jié)果： {'Runoob', 'Taobao', 'Google'} <class 'set'> ['Taobao', 'Runoob'] {'Runoob', 'Taobao', 'Google'} """

源代碼：

def sample(self, population, k):"""Chooses k unique random elements from a population sequence or set.Returns a new list containing elements from the population whileleaving the original population unchanged. The resulting list isin selection order so that all sub-slices will also be valid randomsamples. This allows raffle winners (the sample) to be partitionedinto grand prize and second place winners (the subslices).Members of the population need not be hashable or unique. If thepopulation contains repeats, then each occurrence is a possibleselection in the sample.To choose a sample in a range of integers, use range as an argument.This is especially fast and space efficient for sampling from alarge population: sample(range(10000000), 60)"""# Sampling without replacement entails tracking either potential# selections (the pool) in a list or previous selections in a set.# When the number of selections is small compared to the# population, then tracking selections is efficient, requiring# only a small set and an occasional reselection. For# a larger number of selections, the pool tracking method is# preferred since the list takes less space than the# set and it doesn't suffer from frequent reselections.if isinstance(population, _Set):population = tuple(population)if not isinstance(population, _Sequence):raise TypeError("Population must be a sequence or set. For dicts, use list(d).")randbelow = self._randbelown = len(population)if not 0 <= k <= n:raise ValueError("Sample larger than population or is negative")result = [None] * ksetsize = 21 # size of a small set minus size of an empty listif k > 5:setsize += 4 ** _ceil(_log(k * 3, 4)) # table size for big setsif n <= setsize:# An n-length list is smaller than a k-length setpool = list(population)for i in range(k): # invariant: non-selected at [0,n-i)j = randbelow(n-i)result[i] = pool[j]pool[j] = pool[n-i-1] # move non-selected item into vacancyelse:selected = set()selected_add = selected.addfor i in range(k):j = randbelow(n)while j in selected:j = randbelow(n)selected_add(j)result[i] = population[j]return result

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