python⽣成四位随机数
有些时候需要给⽤户⽣成四位随机数字，这⾥在python中我们可以根据python⾃带的标准库random和string来实现。
random
1# random.choice
2def choice(self, seq):
3"""Choose a random element from a non-empty sequence."""
4try:
5            i = self._randbelow(len(seq))
6except ValueError:
7raise IndexError('Cannot choose from an empty sequence') from None
8return seq[i]
1# random.choices
2def choices(self, population, weights=None, *, cum_weights=None, k=1):
3"""Return a k sized list of population elements chosen with replacement.
4
5        If the relative weights or cumulative weights are not specified,
6        the selections are made with equal probability.
7
8"""
9        random = self.random
10if cum_weights is None:
11if weights is None:
12                _int = int
13                total = len(population)
14return [population[_int(random() * total)] for i in range(k)]
15            cum_weights = list(_itertools.accumulate(weights))
16elif weights is not None:
17raise TypeError('Cannot specify both weights and cumulative weights')
18if len(cum_weights) != len(population):
19raise ValueError('The number of weights does not match the population')
20        bisect = _bisect.bisect
21        total = cum_weights[-1]
22        hi = len(cum_weights) - 1
23return [population[bisect(cum_weights, random() * total, 0, hi)]
24for i in range(k)]
1# random.sample
2
3def sample(self, population, k):
4"""Chooses k unique random elements from a population sequence or set.
5
6        Returns a new list containing elements from the population while
7        leaving the original population unchanged.  The resulting list is
8        in selection order so that all sub-slices will also be valid random
9        samples.  This allows raffle winners (the sample) to be partitioned
10        into grand prize and second place winners (the subslices).
11
12        Members of the population need not be hashable or unique.  If the
13        population contains repeats, then each occurrence is a possible
14        selection in the sample.
15
16        To choose a sample in a range of integers, use range as an argument.
17        This is especially fast and space efficient for sampling from a
18        large population:  sample(range(10000000), 60)
19"""
20
21# Sampling without replacement entails tracking either potential
22# selections (the pool) in a list or previous selections in a set.
23
24# When the number of selections is small compared to the
25# population, then tracking selections is efficient, requiring
26# only a small set and an occasional reselection.  For
27# a larger number of selections, the pool tracking method is
28# preferred since the list takes less space than the
29# set and it doesn't suffer from frequent reselections.
30
31if isinstance(population, _Set):
32            population = tuple(population)
33if not isinstance(population, _Sequence):
34raise TypeError("Population must be a sequence or set.  For dicts, use list(d).")
35        randbelow = self._randbelow
36        n = len(population)
37if not 0 <= k <= n:
38raise ValueError("Sample larger than population or is negative")
39        result = [None] * k
40        setsize = 21        # size of a small set minus size of an empty list
python生成1到100之间随机数41if k > 5:
42            setsize += 4 ** _ceil(_log(k * 3, 4)) # table size for big sets
43if n <= setsize:
44# An n-length list is smaller than a k-length set
45            pool = list(population)
46for i in range(k):        # invariant:  non-selected at [0,n-i)
47                j = randbelow(n-i)
48                result[i] = pool[j]
49                pool[j] = pool[n-i-1]  # move non-selected item into vacancy
50else:
51            selected = set()
52            selected_add = selected.add
53for i in range(k):
54                j = randbelow(n)
55while j in selected:
56                    j = randbelow(n)
57                selected_add(j)
58                result[i] = population[j]
59return result
从上⾯这三个函数看来，都可以在给定的⼀个数字集内随机产⽣四位数字。三种⽅法如下：
1import string
2import random
3
4# ⽅法⼀
5 seeds = string.digits
6 random_str = []
7for i in range(4):
8    random_str.append(random.choice(seeds))
9print("".join(random_str))
10
11# ⽅法⼆
12 seeds = string.digits
13 random_str = random.choices(seeds, k=4)
14print("".join(random_str))
15
16# ⽅法三
17 seeds = string.digits
18 random_str = random.sample(seeds, k=4)
19print("".join(random_str))
说明⼀下：string.digits是⼀个定义好的数字字符串，就是从"0123456789"。
1"""
2whitespace -- a string containing all ASCII whitespace
3ascii_lowercase -- a string containing all ASCII lowercase letters
4ascii_uppercase -- a string containing all ASCII uppercase letters
5ascii_letters -- a string containing all ASCII letters
6digits -- a string containing all ASCII decimal digits
7hexdigits -- a string containing all ASCII hexadecimal digits
8octdigits -- a string containing all ASCII octal digits
9punctuation -- a string containing all ASCII punctuation characters
10printable -- a string containing all ASCII characters considered printable
11"""
12
13# Some strings for ctype-style character classification
14 whitespace = ' \t\n\r\v\f'
15 ascii_lowercase = 'abcdefghijklmnopqrstuvwxyz'
16 ascii_uppercase = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
17 ascii_letters = ascii_lowercase + ascii_uppercase
18 digits = '0123456789'
19 hexdigits = digits + 'abcdef' + 'ABCDEF'
20 octdigits = '01234567'
21 punctuation = r"""!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~"""
22 printable = digits + ascii_letters + punctuation + whitespace
上述三种⽅式虽说都可以⽣成随机数，但是choice和choices随机取得数字是可重复的，⽽sample⽅法的随机数是不会重复的。
这个是他们之间的区别之⼀。

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