random函数汇总

1. random.random

random.random()用于生成一个0到1之间的随机浮点数：0<=n<1

>>> random.random() 0.96296

2. random.uniform

random.uniform(a,b)用于生成一个指定范围内的随机浮点数，若a<b，则a<=n<=b;若a>b,则b<=n<=a.

>>> random.uniform(12,5) 6.2529 >>> random.uniform(5,12) 5.9382 >>> random.uniform(5,5) 5.0

3. random.randint

random.randint(a,b)用于生成一个指定范围内的整数：a<=n<=b；下限必须小于等于上限值，random.randint(20,10)是错误的

>>> random.randint(10,10) 10 >>> random.randint(10,21) 15 >>> random.randint(100,100) 100

4. random.randrange

random.randrange([start],[stop],[step])从指定范围内，按指定基数递增的集合中获取一个随机数。等于random.choice(range([start],[stop],[step]))

>>> random.randrange(1,100,10) 61 >>> random.randrange(1,100,10) 21 >>> random.choice(range(1,100,10)) 71 >>> random.choice(range(1,100,10)) 41 >>> random.randrange(100,100) Traceback (most recent call last):   File "<stdin>", line 1, in <module>   File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/random.py", line 199, in randrange     raise ValueError("empty range for randrange() (%d,%d, %d)" % (istart, istop, width)) ValueError: empty range for randrange() (100,100, 0)

5. random.choice

random.choice(seq)从序列中获取随机一个元素。

choices(population, weights=None, *, cum_weights=None, k=1)这个方法平时比较少用，population是一个可迭代对象，weights是相对权重，cum_weights是累积权重，k表示随机获取的个数。

例如，相对权重“[10, 5, 30, 5]“相当于累积权重“[10, 15, 45, 50]“。 在内部，相对权重在进行选择之前会转换为累积权重，因此提供累积权重可以节省工作量。random官方文档

需要注意2点：

1. weights和cum_weights不能同时使用。

2.population与weights，population与cum_weights需一一对应

>>> random.choice("abcde") 'd' >>> random.choice([1,2,3,4]) 2 >>> random.choice((1.1,2.2,3.3,4.4)) 3.3 >>> for i in range(10): ... print(random.choices("abcd",weights=[1,1,7,1],cum_weights=[70,10,5,15],k=1)) ... Traceback (most recent call last): File "<stdin>", line 2, in <module> File "E:\Programs\Python36\lib\random.py", line 356, in choices raise TypeError('Cannot specify both weights and cumulative weights') TypeError: Cannot specify both weights and cumulative weights >>> for i in range(10): ... print(random.choices(['x','y','z'],cum_weights=[70,10,5,15],k=1)) ... Traceback (most recent call last): File "<stdin>", line 2, in <module> File "E:\Programs\Python36\lib\random.py", line 358, in choices raise ValueError('The number of weights does not match the population') ValueError: The number of weights does not match the population >>> for i in range(10): ... print(random.choices(['x','y','z','u'],cum_weights=[70,10,5],k=1)) ... Traceback (most recent call last): File "<stdin>", line 2, in <module> File "E:\Programs\Python36\lib\random.py", line 358, in choices raise ValueError('The number of weights does not match the population') ValueError: The number of weights does not match the population >>> for i in range(10): ... print(random.choices("abcd",weights=[1,1,7,1],k=1)) ... ['b'] ['c'] ['c'] ['c'] ['c'] ['c'] ['a'] ['a'] ['c'] ['c'] 由上可以明显发现，随机获得c的概率更高 >>> for i in range(10): ... print(random.choices(['x','y','z','w'],cum_weights=[70,10,5,15],k=1)) ... ['w'] ['w'] ['w'] ['w'] ['x'] ['x'] ['w'] ['x'] ['x'] ['x']

6. random.shuffle

random.shuffle(x[, random])用于将一个列表中的元素打乱

>>> l=['a','b','c','d','e'] >>> random.shuffle(l) >>> l ['d', 'b', 'e', 'c', 'a']

7. random.sample

random.sample(seq,k)从指定序列中随机获取指定长度的，且不重复出现的片段

>>> l=['a','b','c','d','e'] >>> s=random.sample(l,2) >>> s ['c', 'a'] >>> l ['a', 'b', 'c', 'd', 'e']

8. random.seed

random.seed(n)用于改变随机数生成器的种子，指定随机数生成时所用的算法。

>>> random.seed(2) >>> random.random() 0.92494 >>> random.random() 0.93494 

9. 实践

写一个函数：随机生成n个整数，n个整数的和等于m

import random def random_num(n, m): # 随机生成n个数字 numbers = [random.random() for _ in range(n)] # 计算出 m 与 n个随机数的商 k = m / sum(numbers) # n个随机数分别乘以k，n个数的和接近于m result = [int(i * k) for i in numbers] # 从result中随机选择一个数，加上m与sum(result)的差数，从而实现sum(result) = m result[random.randint(0,n-1)] += m - sum(result) print("sum(result)=", sum(result)) print("result=", result) >>> random_num(5, 100) sum(result)= 100 result= [5, 21, 15, 33, 26] >>> random_num(2, 15) sum(result)= 15 result= [2, 13] >>>