之前我们讲到了time模块
那我们今天来谈一谈random模块
random模块可谓是python模块中最为简单的一个模块了
那，接下来我们步入正堂
首先，我们要导入random包来使用相关函数
import random
接下来我们使用help函数来看看random包里都有一些什么呢
print(help(random))
Help on module random: NAME random - Random variable generators. MODULE REFERENCE https://docs.python.org/3.8/library/random The following documentation is automatically generated from the Python source files. It may be incomplete,incorrect or include features that are considered implementation detail and may vary between Python implementations. When in doubt,consult the module reference at the location listed above. DESCRIPTION integers -------- uniform within range sequences --------- pick random element pick random sample pick weighted random sample generate random permutation distributions on the real line: ------------------------------ uniform triangular normal (Gaussian) lognormal negative exponential gamma beta pareto Weibull distributions on the circle (angles 0 to 2pi) --------------------------------------------- circular uniform von Mises General notes on the underlying Mersenne Twister core generator: * The period is 2**19937-1. * It is one of the most extensively tested generators existence. * The random() method is implemented in C,executes a single Python step,and is,therefore,threadsafe. CLASSES _random.Random(builtins.object) Random SystemRandom class Random(_random.Random) | Random(x=None) | | Random number generator base used by bound module functions. | | Used to instantiate instances of Random to get generators that don't | share state. | | Class Random can also be subclassed if you want to use a different basic | generator of your own devising: that case,override the following | methods: random(),seed(),getstate(), setstate(). | Optionally,implement a getrandbits() method so that randrange() | can cover arbitrarily large ranges. | | Method resolution order: | Random | _random.Random | builtins.object | | Methods defined here: | | __getstate__(self) | # Issue 17489: Since __reduce__ was defined to fix #759889 this is no | longer called; we leave it here because it has been here since random was | rewritten back in 2001 and why risk breaking something. | | __init__(self,x=None) | Initialize an instance. | | Optional argument x controls seeding,as for Random.seed(). | | __reduce__(self) | Helper pickle. | | __setstate__(self,state) | | betavariate(self,alpha,beta) | Beta distribution. | | Conditions on the parameters are alpha > 0 and beta > 0. | Returned values range between 0 and 1. | | choice(self,seq) | Choose a random element from a non-empty sequence. | | choices(self,population,weights=None,*,cum_weights=None,k=1) | Return a k sized list of population elements chosen with replacement. | | If the relative weights or cumulative weights are not specified,| the selections are made with equal probability. | | expovariate(self,lambd) | Exponential distribution. | | lambd is 1.0 divided by the desired mean. It should be | nonzero. (The parameter would be called "lambda",but that is | a reserved word in Python.) Returned values range 0 to | positive infinity if lambd is positive,1)"> negative | infinity to 0 negative. | | gammavariate(self,1)"> Gamma distribution. Not the gamma function! | | Conditions on the parameters are alpha > 0 0. | | The probability distribution function : | | x ** (alpha - 1) * math.exp(-x / beta) | pdf(x) = -------------------------------------- | math.gamma(alpha) * beta ** alpha | | gauss(self,mu,sigma) | Gaussian distribution. | | mu is the mean,1)">and sigma is the standard deviation. This is | slightly faster than the normalvariate() function. | | Not thread-safe without a lock around calls. | | getstate(self) | Return internal state; can be passed to setstate() later. | | lognormvariate(self,1)"> Log normal distribution. | | If you take the natural logarithm of this distribution,youll get a | normal distribution with mean mu standard deviation sigma. | mu can have any value,1)"> sigma must be greater than zero. | | normalvariate(self,1)"> Normal distribution. | | mu the standard deviation. | | paretovariate(self,alpha) | Pareto distribution. alpha the shape parameter. | | randint(self,a,b) | Return random integer range [a,b],including both end points. | | randrange(self,start,stop=None,step=1,_int=<class int'>) | Choose a random item range(start,stop[,step]). | | This fixes the problem with randint() which includes the | endpoint; in Python this is usually what you want. | | sample(self,k) | Chooses k unique random elements from a population sequence set. | | Returns a new list containing elements from the population while | leaving the original population unchanged. The resulting list is | in selection order so that all sub-slices will also be valid random | samples. This allows raffle winners (the sample) to be partitioned | into grand prize second place winners (the subslices). | | Members of the population need not be hashable unique. If the | population contains repeats,then each occurrence a possible | selection the sample. | | To choose a sample a range of integers,use range as an argument. | This is especially fast and space efficient for sampling a | large population: sample(range(10000000),60) | | seed(self,a=None,version=2) | Initialize internal state hashable object. | | None or no argument seeds from current time or an operating | system specific randomness source available. | | If *a* an int,all bits are used. | | For version 2 (the default),all of the bits are used if *a* a str,| bytes,1)">or bytearray. For version 1 (provided reproducing random | sequences from older versions of Python),the algorithm for str and | bytes generates a narrower range of seeds. | | setstate(self,state) | Restore internal state object returned by getstate(). | | shuffle(self,x,random=None) | Shuffle list x in place,1)">return None. | | Optional argument random is a 0-argument function returning a | random float in [0.0,1.0); if it the default None,the | standard random.random will be used. | | triangular(self,low=0.0,high=1.0,mode= Triangular distribution. | | Continuous distribution bounded by given lower upper limits,| and having a given mode value in-between. | | http://en.wikipedia.org/wiki/Triangular_distribution | | uniform(self,b) | Get a random number in the range [a,b) [a,b] depending on rounding. | | vonmisesvariate(self,kappa) | Circular data distribution. | | mu is the mean angle,expressed in radians between 0 and 2*pi,1)">and | kappa is the concentration parameter,which must be greater than or | equal to zero. If kappa equal to zero,this distribution reduces | to a uniform random angle over the range 0 to 2*pi. | | weibullvariate(self,1)"> Weibull distribution. | | alpha is the scale parameter and beta the shape parameter. | | ---------------------------------------------------------------------- | Class methods defined here: | | __init_subclass__(**kwargs) builtins.type | Control how subclasses generate random integers. | | The algorithm a subclass can use depends on the random() and/or | getrandbits() implementation available to it determines | whether it can generate random integers arbitrarily large | ranges. | | ---------------------------------------------------------------------- | Data descriptors defined here: | | __dict__ | dictionary for instance variables ( defined) | | __weakref__ | list of weak references to the object ( defined) | | ---------------------------------------------------------------------- | Data other attributes defined here: | | VERSION = 3 | | ---------------------------------------------------------------------- | Methods inherited _random.Random: | | __getattribute__(self,name,/ Return getattr(self,name). | | getrandbits(self,k,1)">) | getrandbits(k) -> x. Generates an int with k random bits. | | random(self,1)">) | random() -> x in the interval [0,1). | | ---------------------------------------------------------------------- | Static methods inherited __new__(*args,**kwargs) builtins.type | Create return a new object. See help(type) accurate signature. SystemRandom(Random) | SystemRandom(x=None) | | Alternate random number generator using sources provided | by the operating system (such as /dev/urandom on Unix or | CryptGenRandom on Windows). | | Not available on all systems (see os.urandom() details). | | SystemRandom | Methods defined here: | | getrandbits(self,k) | getrandbits(k) -> x. Generates an int with k random bits. | | getstate = _notimplemented(self,*args,**kwds) | | random(self) | Get the next random number in the range [0.0,1.0). | | seed(self,1)">kwds) | Stub method. Not used a system random number generator. | | setstate = _notimplemented(self,1)">kwds) | | ---------------------------------------------------------------------- | Methods inherited Random: | | ) | | shuffle(self,1)"> the shape parameter. | | ---------------------------------------------------------------------- | Class methods inherited ranges. | | ---------------------------------------------------------------------- | Data descriptors inherited and other attributes inherited Random: | | VERSION = 3 | | ---------------------------------------------------------------------- | Methods inherited accurate signature. FUNCTIONS betavariate(alpha,beta) method of Random instance Beta distribution. Conditions on the parameters are alpha > 0 0. Returned values range between 0 . choice(seq) method of Random instance Choose a random element empty sequence. choices(population,weights=None,1)">) method of Random instance Return a k sized list of population elements chosen with replacement. If the relative weights divided by the desired mean. It should be nonzero. (The parameter would be called a reserved word 0 to positive infinity negative infinity to 0 negative. gammavariate(alpha,beta) method of Random instance Gamma distribution. Not the gamma function! Conditions on the parameters are alpha > 0 0. The probability distribution function : x ** (alpha - 1) * math.exp(-x / beta) pdf(x) = -------------------------------------- math.gamma(alpha) * beta ** alpha gauss(mu,sigma) method of Random instance Gaussian distribution. mu slightly faster than the normalvariate() function. Not thread-safe without a lock around calls. getrandbits(k,/) method of Random instance getrandbits(k) -> x. Generates an int with k random bits. getstate() method of Random instance Return internal state; can be passed to setstate() later. lognormvariate(mu,sigma) method of Random instance Log normal distribution. If you take the natural logarithm of this distribution,youll get a normal distribution with mean mu standard deviation sigma. mu can have any value,1)"> sigma must be greater than zero. normalvariate(mu,sigma) method of Random instance Normal distribution. mu the standard deviation. paretovariate(alpha) method of Random instance Pareto distribution. alpha the shape parameter. randint(a,b) method of Random instance Return random integer ). randrange(start,stop=None,1)">) method of Random instance Choose a random item what you want. sample(population,k) method of Random instance Chooses k unique random elements set. Returns a new list containing elements while leaving the original population unchanged. The resulting list is slices will also be valid random samples. This allows raffle winners (the sample) to be partitioned into grand prize second place winners (the subslices). Members of the population need unique. If the population contains repeats,then each occurrence a possible selection the sample. To choose a sample a large population: sample(range(10000000),1)">) seed(a=None,1)">) method of Random instance Initialize internal state hashable object. None an operating system specific randomness source available. If *a* reproducing random sequences bytes generates a narrower range of seeds. setstate(state) method of Random instance Restore internal state object returned by getstate(). shuffle(x,random=None) method of Random instance Shuffle list x None. Optional argument random argument function returning a random float None) method of Random instance Triangular distribution. Continuous distribution bounded by given lower between. http://en.wikipedia.org/wiki/Triangular_distribution uniform(a,b) method of Random instance Get a random number kappa equal to zero. If kappa pi. weibullvariate(alpha,beta) method of Random instance Weibull distribution. alpha the shape parameter. DATA __all__ = [Random',seedrandomuniformrandintchoice'\lib\random.py None Process finished with exit code 0
那么接下来我们就来挨个看看
1. random.random() 随机产生一个0~1之内的小数
print(help(random.random)) print(random.random()) 产生0 ~ 1 之间的小数
1 Help on built- function random: 2 3 random() method of random.Random instance 4 random() -> x ). 5 6 None 7 0.46211947242172047
2. random.randint(a,b) 随机产生一个在指定范围内的整数
(help(random.randint)) print(random.randint(0,8)) 产生0 ~ 8的整数(包括 8 )
Help on method randint module random: randint(a,b) method of random.Random instance Return random integer View Code
3.random.randrange(a,b) 随机产生一个在指定范围内的整数
(help(random.randrange)) print(random.randrange(0,1)"> 产生0 ~ 8的整数(不包括 8 )
Help on method randrange module random: randrange(start,1)">) method of random.Random instance Choose a random item what you want. None 3 Process finished with exit code 0
但是这个与上者的区别可要注意看了，前者包括b，后者不包括b
我们通常大多数情况下基本会采用randrange(a,b)这种形式的
4.random.choice() 在所给序列里随机挑出一个随机数（字符）
(help(random.choice)) print(random.choice(21414')) 在所给序列里随机挑出一个随机数（字符） qwqwfqcxws)) print(random.choice([1,2,3,[2,3],[5,6]]))
Help on method choice module random: choice(seq) method of random.Random instance Choose a random element empty sequence. None 4 w [5,6]
5.random.sample() 在所给序列里随机挑出指定个随机数（字符），并以列表形式返回
(help(random.sample)) print(random.sample(qeqrqwt 在所给序列里随机挑出自定义个随机数（字符）
Help on method sample module random: sample(population,k) method of random.Random instance Chooses k unique random elements set. Returns a new list containing elements leaving the original population unchanged. The resulting list is slices will also be valid random samples. This allows raffle winners (the sample) to be partitioned into grand prize second place winners (the subslices). Members of the population need unique. If the population contains repeats,1)"> a possible selection the sample. To choose a sample a large population: sample(range(10000000),1)">) None [qte']
6.random.shuffe() 将所给序列随机乱序,这要注意的一点就是它并没有返回值
(help(random.sample)) a=[1,4,5,6,[7,7,8,8]] random.shuffle(a) 将所给序列随机乱序 print(a)
Help on method sample )
None
[6,1,8],5]
随机数模块我们便到此结束。
下面，我们可以使用上述知识来简单的写一个随机验证码的程序：
import random 实现验证码的实现 def auth_code(): code = '' for i in range(4): code_choice = [str(random.randint(0,9)),chr(random.randint(97,122))] 为了实现数字和字母的随机分布 code += random.choice(code_choice) code string auth_code_2(): code_choice = string.ascii_lowercase + string.digits code = random.sample(code_choice,4) code=''.join(code) auth_code_3(): code = ): if i == random.randint(0,1)">): code += str(random.randint(0,9)) else: code += chr(random.randint(97,122)) code (auth_code()) (auth_code_2()) print(auth_code_3())