radom模块提供了随机生成对象的方法
Help on module random: NAME random - Random variable generators. FILE /usr/local/lib/python2.7/random.py MODULE DOCS http://docs.python.org/library/random DESCRIPTION integers -------- uniform within range sequences --------- pick random element pick 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 in existence. * Without a direct way to compute N steps forward, the semantics of jumpahead(n) are weakened to simply jump to another distant state and rely on the large period to avoid overlapping sequences. * The random() method is implemented in C, executes in a single Python step, and is, therefore, threadsafe. CLASSES _random.Random(__builtin__.object) Random SystemRandom WichmannHill class Random(_random.Random) | Random number generator base class used by bound module functions. | | Used to instantiate instances of Random to get generators that don't | share state. Especially useful for multi-threaded programs, creating | a different instance of Random for each thread, and using the jumpahead() | method to ensure that the generated sequences seen by each thread don't | overlap. | | Class Random can also be subclassed if you want to use a different basic | generator of your own devising: in that case, override the following | methods: random(), seed(), getstate(), setstate() and jumpahead(). | Optionally, implement a getrandbits() method so that randrange() can cover | arbitrarily large ranges. | | Method resolution order: | Random | _random.Random | __builtin__.object | | Methods defined here: | | __getstate__(self) | | __init__(self, x=None) | Initialize an instance. | | Optional argument x controls seeding, as for Random.seed(). | | __reduce__(self) | | __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. | | 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 from 0 to | positive infinity if lambd is positive, and from negative | infinity to 0 if lambd is negative. | | gammavariate(self, alpha, beta) | Gamma distribution. Not the gamma function! | | Conditions on the parameters are alpha > 0 and beta > 0. | | The probability distribution function is: | | x ** (alpha - 1) * math.exp(-x / beta) | pdf(x) = -------------------------------------- | math.gamma(alpha) * beta ** alpha | | gauss(self, mu, sigma) | Gaussian distribution. | | mu is the mean, 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. | | jumpahead(self, n) | Change the internal state to one that is likely far away | from the current state. This method will not be in Py3.x, | so it is better to simply reseed. | | lognormvariate(self, mu, sigma) | Log normal distribution. | | If you take the natural logarithm of this distribution, you'll get a | normal distribution with mean mu and standard deviation sigma. | mu can have any value, and sigma must be greater than zero. | | normalvariate(self, mu, sigma) | Normal distribution. | | mu is the mean, and sigma is the standard deviation. | | paretovariate(self, alpha) | Pareto distribution. alpha is the shape parameter. | | randint(self, a, b) | Return random integer in range [a, b], including both end points. | | randrange(self, start, stop=None, step=1, _int=<type 'int'>, _maxwidth=9007199254740992L) | Choose a random item from range(start, stop[, step]). | | This fixes the problem with randint() which includes the | endpoint; in Python this is usually not what you want. | | sample(self, population, k) | Chooses k unique random elements from a population sequence. | | 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 and second place winners (the subslices). | | Members of the population need not be hashable or unique. If the | population contains repeats, then each occurrence is a possible | selection in the sample. | | To choose a sample in a range of integers, use xrange as an argument. | This is especially fast and space efficient for sampling from a | large population: sample(xrange(10000000), 60) | | seed(self, a=None) | Initialize internal state from hashable object. | | None or no argument seeds from current time or from an operating | system specific randomness source if available. | | If a is not None or an int or long, hash(a) is used instead. | | setstate(self, state) | Restore internal state from object returned by getstate(). | | shuffle(self, x, random=None) | x, random=random.random -> shuffle list x in place; return None. | | Optional arg random is a 0-argument function returning a random | float in [0.0, 1.0); by default, the standard random.random. | | triangular(self, low=0.0, high=1.0, mode=None) | Triangular distribution. | | Continuous distribution bounded by given lower and upper limits, | and having a given mode value in-between. | | http://en.wikipedia.org/wiki/Triangular_distribution | | uniform(self, a, b) | Get a random number in the range [a, b) or [a, b] depending on rounding. | | vonmisesvariate(self, mu, kappa) | Circular data distribution. | | mu is the mean angle, expressed in radians between 0 and 2*pi, and | kappa is the concentration parameter, which must be greater than or | equal to zero. If kappa is equal to zero, this distribution reduces | to a uniform random angle over the range 0 to 2*pi. | | weibullvariate(self, alpha, beta) | Weibull distribution. | | alpha is the scale parameter and beta is the shape parameter. | | ---------------------------------------------------------------------- | Data descriptors defined here: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | VERSION = 3 | | ---------------------------------------------------------------------- | Methods inherited from _random.Random: | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | getrandbits(...) | getrandbits(k) -> x. Generates a long int with k random bits. | | random(...) | random() -> x in the interval [0, 1). | | ---------------------------------------------------------------------- | Data and other attributes inherited from _random.Random: | | __new__ = <built-in method __new__ of type object> | T.__new__(S, ...) -> a new object with type S, a subtype of T class SystemRandom(Random) | 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() for details). | | Method resolution order: | SystemRandom | Random | _random.Random | __builtin__.object | | Methods defined here: | | getrandbits(self, k) | getrandbits(k) -> x. Generates a long int with k random bits. | | getstate = _notimplemented(self, *args, **kwds) | | jumpahead = _stub(self, *args, **kwds) | | random(self) | Get the next random number in the range [0.0, 1.0). | | seed = _stub(self, *args, **kwds) | | setstate = _notimplemented(self, *args, **kwds) | | ---------------------------------------------------------------------- | Methods inherited from Random: | | __getstate__(self) | | __init__(self, x=None) | Initialize an instance. | | Optional argument x controls seeding, as for Random.seed(). | | __reduce__(self) | | __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. | | 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 from 0 to | positive infinity if lambd is positive, and from negative | infinity to 0 if lambd is negative. | | gammavariate(self, alpha, beta) | Gamma distribution. Not the gamma function! | | Conditions on the parameters are alpha > 0 and beta > 0. | | The probability distribution function is: | | x ** (alpha - 1) * math.exp(-x / beta) | pdf(x) = -------------------------------------- | math.gamma(alpha) * beta ** alpha | | gauss(self, mu, sigma) | Gaussian distribution. | | mu is the mean, and sigma is the standard deviation. This is | slightly faster than the normalvariate() function. | | Not thread-safe without a lock around calls. | | lognormvariate(self, mu, sigma) | Log normal distribution. | | If you take the natural logarithm of this distribution, you'll get a | normal distribution with mean mu and standard deviation sigma. | mu can have any value, and sigma must be greater than zero. | | normalvariate(self, mu, sigma) | Normal distribution. | | mu is the mean, and sigma is the standard deviation. | | paretovariate(self, alpha) | Pareto distribution. alpha is the shape parameter. | | randint(self, a, b) | Return random integer in range [a, b], including both end points. | | randrange(self, start, stop=None, step=1, _int=<type 'int'>, _maxwidth=9007199254740992L) | Choose a random item from range(start, stop[, step]). | | This fixes the problem with randint() which includes the | endpoint; in Python this is usually not what you want. | | sample(self, population, k) | Chooses k unique random elements from a population sequence. | | 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 and second place winners (the subslices). | | Members of the population need not be hashable or unique. If the | population contains repeats, then each occurrence is a possible | selection in the sample. | | To choose a sample in a range of integers, use xrange as an argument. | This is especially fast and space efficient for sampling from a | large population: sample(xrange(10000000), 60) | | shuffle(self, x, random=None) | x, random=random.random -> shuffle list x in place; return None. | | Optional arg random is a 0-argument function returning a random | float in [0.0, 1.0); by default, the standard random.random. | | triangular(self, low=0.0, high=1.0, mode=None) | Triangular distribution. | | Continuous distribution bounded by given lower and upper limits, | and having a given mode value in-between. | | http://en.wikipedia.org/wiki/Triangular_distribution | | uniform(self, a, b) | Get a random number in the range [a, b) or [a, b] depending on rounding. | | vonmisesvariate(self, mu, kappa) | Circular data distribution. | | mu is the mean angle, expressed in radians between 0 and 2*pi, and | kappa is the concentration parameter, which must be greater than or | equal to zero. If kappa is equal to zero, this distribution reduces | to a uniform random angle over the range 0 to 2*pi. | | weibullvariate(self, alpha, beta) | Weibull distribution. | | alpha is the scale parameter and beta is the shape parameter. | | ---------------------------------------------------------------------- | Data descriptors inherited from Random: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) | | ---------------------------------------------------------------------- | Data and other attributes inherited from Random: | | VERSION = 3 | | ---------------------------------------------------------------------- | Methods inherited from _random.Random: | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | ---------------------------------------------------------------------- | Data and other attributes inherited from _random.Random: | | __new__ = <built-in method __new__ of type object> | T.__new__(S, ...) -> a new object with type S, a subtype of T class WichmannHill(Random) | Method resolution order: | WichmannHill | Random | _random.Random | __builtin__.object | | Methods defined here: | | getstate(self) | Return internal state; can be passed to setstate() later. | | jumpahead(self, n) | Act as if n calls to random() were made, but quickly. | | n is an int, greater than or equal to 0. | | Example use: If you have 2 threads and know that each will | consume no more than a million random numbers, create two Random | objects r1 and r2, then do | r2.setstate(r1.getstate()) | r2.jumpahead(1000000) | Then r1 and r2 will use guaranteed-disjoint segments of the full | period. | | random(self) | Get the next random number in the range [0.0, 1.0). | | seed(self, a=None) | Initialize internal state from hashable object. | | None or no argument seeds from current time or from an operating | system specific randomness source if available. | | If a is not None or an int or long, hash(a) is used instead. | | If a is an int or long, a is used directly. Distinct values between | 0 and 27814431486575L inclusive are guaranteed to yield distinct | internal states (this guarantee is specific to the default | Wichmann-Hill generator). | | setstate(self, state) | Restore internal state from object returned by getstate(). | | whseed(self, a=None) | Seed from hashable object's hash code. | | None or no argument seeds from current time. It is not guaranteed | that objects with distinct hash codes lead to distinct internal | states. | | This is obsolete, provided for compatibility with the seed routine | used prior to Python 2.1. Use the .seed() method instead. | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | VERSION = 1 | | ---------------------------------------------------------------------- | Methods inherited from Random: | | __getstate__(self) | | __init__(self, x=None) | Initialize an instance. | | Optional argument x controls seeding, as for Random.seed(). | | __reduce__(self) | | __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. | | 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 from 0 to | positive infinity if lambd is positive, and from negative | infinity to 0 if lambd is negative. | | gammavariate(self, alpha, beta) | Gamma distribution. Not the gamma function! | | Conditions on the parameters are alpha > 0 and beta > 0. | | The probability distribution function is: | | x ** (alpha - 1) * math.exp(-x / beta) | pdf(x) = -------------------------------------- | math.gamma(alpha) * beta ** alpha | | gauss(self, mu, sigma) | Gaussian distribution. | | mu is the mean, and sigma is the standard deviation. This is | slightly faster than the normalvariate() function. | | Not thread-safe without a lock around calls. | | lognormvariate(self, mu, sigma) | Log normal distribution. | | If you take the natural logarithm of this distribution, you'll get a | normal distribution with mean mu and standard deviation sigma. | mu can have any value, and sigma must be greater than zero. | | normalvariate(self, mu, sigma) | Normal distribution. | | mu is the mean, and sigma is the standard deviation. | | paretovariate(self, alpha) | Pareto distribution. alpha is the shape parameter. | | randint(self, a, b) | Return random integer in range [a, b], including both end points. | | randrange(self, start, stop=None, step=1, _int=<type 'int'>, _maxwidth=9007199254740992L) | Choose a random item from range(start, stop[, step]). | | This fixes the problem with randint() which includes the | endpoint; in Python this is usually not what you want. | | sample(self, population, k) | Chooses k unique random elements from a population sequence. | | 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 and second place winners (the subslices). | | Members of the population need not be hashable or unique. If the | population contains repeats, then each occurrence is a possible | selection in the sample. | | To choose a sample in a range of integers, use xrange as an argument. | This is especially fast and space efficient for sampling from a | large population: sample(xrange(10000000), 60) | | shuffle(self, x, random=None) | x, random=random.random -> shuffle list x in place; return None. | | Optional arg random is a 0-argument function returning a random | float in [0.0, 1.0); by default, the standard random.random. | | triangular(self, low=0.0, high=1.0, mode=None) | Triangular distribution. | | Continuous distribution bounded by given lower and upper limits, | and having a given mode value in-between. | | http://en.wikipedia.org/wiki/Triangular_distribution | | uniform(self, a, b) | Get a random number in the range [a, b) or [a, b] depending on rounding. | | vonmisesvariate(self, mu, kappa) | Circular data distribution. | | mu is the mean angle, expressed in radians between 0 and 2*pi, and | kappa is the concentration parameter, which must be greater than or | equal to zero. If kappa is equal to zero, this distribution reduces | to a uniform random angle over the range 0 to 2*pi. | | weibullvariate(self, alpha, beta) | Weibull distribution. | | alpha is the scale parameter and beta is the shape parameter. | | ---------------------------------------------------------------------- | Data descriptors inherited from Random: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) | | ---------------------------------------------------------------------- | Methods inherited from _random.Random: | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | getrandbits(...) | getrandbits(k) -> x. Generates a long int with k random bits. | | ---------------------------------------------------------------------- | Data and other attributes inherited from _random.Random: | | __new__ = <built-in method __new__ of type object> | T.__new__(S, ...) -> a new object with type S, a subtype of T FUNCTIONS betavariate(self, alpha, beta) method of Random instance Beta distribution. Conditions on the parameters are alpha > 0 and beta > 0. Returned values range between 0 and 1. choice(self, seq) method of Random instance Choose a random element from a non-empty sequence. expovariate(self, lambd) method of Random instance 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 from 0 to positive infinity if lambd is positive, and from negative infinity to 0 if lambd is negative. gammavariate(self, alpha, beta) method of Random instance Gamma distribution. Not the gamma function! Conditions on the parameters are alpha > 0 and beta > 0. The probability distribution function is: x ** (alpha - 1) * math.exp(-x / beta) pdf(x) = -------------------------------------- math.gamma(alpha) * beta ** alpha gauss(self, mu, sigma) method of Random instance Gaussian distribution. mu is the mean, and sigma is the standard deviation. This is slightly faster than the normalvariate() function. Not thread-safe without a lock around calls. getrandbits(...) getrandbits(k) -> x. Generates a long int with k random bits. getstate(self) method of Random instance Return internal state; can be passed to setstate() later. jumpahead(self, n) method of Random instance Change the internal state to one that is likely far away from the current state. This method will not be in Py3.x, so it is better to simply reseed. lognormvariate(self, mu, sigma) method of Random instance Log normal distribution. If you take the natural logarithm of this distribution, you'll get a normal distribution with mean mu and standard deviation sigma. mu can have any value, and sigma must be greater than zero. normalvariate(self, mu, sigma) method of Random instance Normal distribution. mu is the mean, and sigma is the standard deviation. paretovariate(self, alpha) method of Random instance Pareto distribution. alpha is the shape parameter. randint(self, a, b) method of Random instance Return random integer in range [a, b], including both end points. random(...) random() -> x in the interval [0, 1). randrange(self, start, stop=None, step=1, _int=<type 'int'>, _maxwidth=9007199254740992L) method of Random instance Choose a random item from range(start, stop[, step]). This fixes the problem with randint() which includes the endpoint; in Python this is usually not what you want. sample(self, population, k) method of Random instance Chooses k unique random elements from a population sequence. 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 and second place winners (the subslices). Members of the population need not be hashable or unique. If the population contains repeats, then each occurrence is a possible selection in the sample. To choose a sample in a range of integers, use xrange as an argument. This is especially fast and space efficient for sampling from a large population: sample(xrange(10000000), 60) seed(self, a=None) method of Random instance Initialize internal state from hashable object. None or no argument seeds from current time or from an operating system specific randomness source if available. If a is not None or an int or long, hash(a) is used instead. setstate(self, state) method of Random instance Restore internal state from object returned by getstate(). shuffle(self, x, random=None) method of Random instance x, random=random.random -> shuffle list x in place; return None. Optional arg random is a 0-argument function returning a random float in [0.0, 1.0); by default, the standard random.random. triangular(self, low=0.0, high=1.0, mode=None) method of Random instance Triangular distribution. Continuous distribution bounded by given lower and upper limits, and having a given mode value in-between. http://en.wikipedia.org/wiki/Triangular_distribution uniform(self, a, b) method of Random instance Get a random number in the range [a, b) or [a, b] depending on rounding. vonmisesvariate(self, mu, kappa) method of Random instance Circular data distribution. mu is the mean angle, expressed in radians between 0 and 2*pi, and kappa is the concentration parameter, which must be greater than or equal to zero. If kappa is equal to zero, this distribution reduces to a uniform random angle over the range 0 to 2*pi. weibullvariate(self, alpha, beta) method of Random instance Weibull distribution. alpha is the scale parameter and beta is the shape parameter. DATA __all__ = ['Random', 'seed', 'random', 'uniform', 'randint', 'choice',...
random.random() # 随机生成0.0到1.0间的的浮点数 random.randrange(start, stop[, step]) # 随机生成start到stop之间的整数（可以指定步长）, start和step不是必须的 random.randint(a, b) # 随机生成a到b之间的整数 random.choice(seq) # 从非空序列seq返回一个随机的元素。如果序列是空的抛出IndexError random.uniform(a, b) # 生成a到b之间的浮点数, a与b都为整形 random.shuffle(items) # 随机打乱一个序列，是原处修改 random.sample(population, k) # 从一个序列中选k个
练习:
>>> random.random() # 返回随机浮点数中的范围0.0 <= x < 1.0 0.8600361379435925 >>> random.randrange(90,100,2) # 随机生成90到100之间的数，步长为2 94 >>> random.randint(10,100) # 10到100之间的整数 62 >>> random.choice([10,20,30,40]) # 从一个序列中返回一个元素 10 >>> random.choice('abbacaedffabcde') 'a' >>> random.uniform(1, 10) # 1,10之间的浮点 6.78650282776282 >> items = ['a','b','c','d','e','f','g'] # 随机打乱一个序列,原处修改 >>> random.shuffle(items) >>> items ['a', 'g', 'b', 'f', 'd', 'e', 'c'] >>> random.sample([1, 2, 3, 4, 5], 3) # 从一个序列中选3个 [4, 1, 5] >>> random.randint(122400,500000) # 随机6位数 307770 >>> random.randrange(122400,500000,2) # 随机6位数偶数 426194 >>> [random.randrange(10) for _ in xrange(20)] # 随机生成20个10以内的数字 [8, 0, 9, 5, 2, 7, 0, 2, 7, 6, 9, 4, 5, 3, 2, 5, 3, 8, 7, 4]
随机生成6位大写或小写字母
import string import random def code(n=6, upper=False): """ 随机生成n位大写或小写字母组成的验证码, 默认小写6位 parmas n: 随机n位, 默认小写6位 return: str """ if not isinstance(n, int): raise TypeError('Must be an integer') if n <= 0: raise ValueError('Must be greater 0') result = ''.join(random.choice(string.ascii_uppercase) for _ in range(n)) if upper: return result else: return result.lower() if __name__ == '__main__': print(code()) # ydtwix print(code(upper=True)) # DWLGRG print(code(8)) # wkveigga print(code(8, upper=True)) # TQCOVKUO