Say I have a list of 200 positive, unique, random integers called masterlist.

I want to generate a list of 10 lists called container so that: l1 has 2 random numbers coming from masterlist, repetitions excluded; l2 has 4 elements, l3 has 6 elements, and so forth.

I know I can create my container list like this:

comb=[[] for i in range(10)]

and that I can select a random value from a list using random.choice().

What is the best Pythonic way to nest the populating process of these 10 lists, so that I create one list, append the correct number of values checking that there are no repetitions, and proceed on to the next?

EDIT

This is my attempt:

comb=[[] for i in range(10)]

for j in range(1,11):

for k in range(0,2*j):

comb[j][k].append(random.choice(masterlist))

What is wrong with this?

解决方案

This should do the trick:

import random

masterlist = [i for i in range(200)] # For example

container = [

random.sample(masterlist, l)

for l in range(2, 21, 2)

]

The container is made up of a list comprehension, setting the variable l to 2, 4, 6 ... 18, 20 using the range() call. Within each 'loop' of the comprehension, the built in random.sample() call does the sampling-without-replacement that you're after.

一.Python自带的random库

1.参生n--m范围内的一个随机数: random.randint(n,m)

2.产生0到1之间的浮点数: random.random()

3.产生n---m之间的浮点数: random.uniform(1.1,5.4)

4.产生从n---m间隔为k的整数: random.randrange(n,m,k)

5.从序列中随机选取一个元素: random.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])

6.在一些特殊的情况下可能对序列进行一次打乱操作: random.shuffle([1,3,5,6,7])

import random

# 产生1 到 10的一个整数型随机数

print( random.randint(1,10) )

# 产生0 到 1之间的随机浮点数

print( random.random() )

# 产生1.1 到 5.4之间的随机浮点数，区间可以不是整数

print( random.uniform(1.1,5.4) )

# 从序列中随机选取一个元素

print( random.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 0]) )

# 生成从1到100的间隔为2的随机整数

print( random.randrange(1,100,2) )

# 将序列a中的元素顺序打乱

a=[1,3,5,6,7]

random.shuffle([1,3,5,6,7])

print(a)

二.numpy库

1.产生N维的均匀分布的随机数: np.random.rand(d1,d2,d3,...,dn)

2.产生n维的正态分布的随机数: np.random.randn(d1,d2,d3,...,dn)

3.产生n--m之间的k个整数：np.random.randint(n,m,k)

4.产生n个0--1之间的随机数: np.random.random(10)

5.从序列中选择数据: np.random.choice([2,5,7,8,9,11,3])

6.把序列中的数据打乱：np.random.shuffle(item)

import numpy asnp

#产生n维的均匀分布的随机数

print(np.random.rand(5,5,5))

#产生n维的正态分布的随机数

print(np.random.randn(5,5,5))

#产生n--m之间的k个整数

print(np.random.randint(1,50,5))

#产生n个0--1之间的随机数

print(np.random.random(10))

#从序列中选择数据

print(np.random.choice([2,5,7,8,9,11,3]))

#把序列中的数据打乱

#np.random.shuffle(item) 不会参数返回值，改变的话是在原列表中修改的

item= [2,5,7,8,9,11,3]

np.random.shuffle(item)

print(item)

# 生成数字
def rand_num():
    return str(random.randint(0, 9))

# 生成大写字母
def rand_upper():
    return chr(random.randint(65, 90))

# 生成小写字母
def rand_lower():
    return chr(random.randint(97, 122))

# 生成n位随机验证码
def rand_verify_code(n=4):
    lst = ["rand_num", "rand_upper", "rand_lower"]
    str = ""
    for i in range(n):
        str += (eval(random.choice(lst))())
    return str

n = int(input('输入验证码的位数: '))
print(rand_verify_code(n))

输入验证码的位数: 9
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