使用python从列表中随机提取x项

问题描述

从两个列表开始，例如:

Starting with two lists such as:

lstOne = [ '1', '2', '3', '4', '5', '6', '7', '8', '9', '10']
lstTwo = [ '1', '2', '3', '4', '5', '6', '7', '8', '9', '10']

我想让用户输入要提取的项目数(占列表总长度的百分比)，以及要从每个列表中随机提取的相同索引.例如说我想要50％的输出是

I want to have the user input how many items they want to extract, as a percentage of the overall list length, and the same indices from each list to be randomly extracted. For example say I wanted 50% the output would be

newLstOne = ['8', '1', '3', '7', '5']
newLstTwo = ['8', '1', '3', '7', '5']

我使用以下代码实现了这一点:

I have achieved this using the following code:

from random import randrange

lstOne = [ '1', '2', '3', '4', '5', '6', '7', '8', '9', '10']
lstTwo = [ '1', '2', '3', '4', '5', '6', '7', '8', '9', '10']

LengthOfList = len(lstOne)
print LengthOfList

PercentageToUse = input("What Percentage Of Reads Do you want to extract? ")
RangeOfListIndices = []

HowManyIndicesToMake = (float(PercentageToUse)/100)*float(LengthOfList)
print HowManyIndicesToMake

for x in lstOne:
    if len(RangeOfListIndices)==int(HowManyIndicesToMake):
        break
    else:
        random_index = randrange(0,LengthOfList)
        RangeOfListIndices.append(random_index)

print RangeOfListIndices


newlstOne = []
newlstTwo = []

for x in RangeOfListIndices:
    newlstOne.append(lstOne[int(x)])
for x in RangeOfListIndices:
    newlstTwo.append(lstTwo[int(x)])

print newlstOne
print newlstTwo

但是我想知道是否有更有效的方法，在我的实际用例中，这是从145,000个项目中进行子采样的.此外，兰德兰奇是否在这种规模上没有偏见?

But I was wondering if there was a more efficient way of doing this, in my actual use case this is subsampling from 145,000 items. Furthermore, is randrange sufficiently free of bias at this scale?

谢谢

推荐答案

问. I want to have the user input how many items they want to extract, as a percentage of the overall list length, and the same indices from each list to be randomly extracted.

A.最直接的方法直接符合您的规范:

A. The most straight-forward approach directly matches your specification:

 percentage = float(raw_input('What percentage? '))
 k = len(data) * percentage // 100
 indicies = random.sample(xrange(len(data)), k)
 new_list1 = [list1[i] for i in indicies]
 new_list2 = [list2[i] for i in indicies]

问.in my actual use case this is subsampling from 145,000 items. Furthermore, is randrange sufficiently free of bias at this scale?

A..在Python 2和Python 3中， random.randrange()函数完全消除了偏差(它使用内部的 _randbelow()可以做出多个随机选择，直到找到无偏差结果的方法.

A. In Python 2 and Python 3, the random.randrange() function completely eliminates bias (it uses the internal _randbelow() method that makes multiple random choices until a bias-free result is found).

在Python 2中， random.sample()函数略有偏差，但仅在四舍五入的最后53位中.在Python 3中， random.sample()函数使用内部的 _randbelow()方法，并且没有偏差.

In Python 2, the random.sample() function is slightly biased but only in the round-off in the last of 53 bits. In Python 3, the random.sample() function uses the internal _randbelow() method and is bias-free.

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