Python标准库random用法精要

random标准库主要提供了伪随机数生成函数和相关的类，同时也提供了SystemRandom类（也可以直接使用os.urandom()函数）来支持生成加密级别要求的不可再现伪随机数序列。 1、random.seed() 初始化随机数生成器。使用相同种子可以生成相同的随机数序列。例如： >>> random.seed(5) >>> random.random() 0.6229016948897019 >>> random.random() 0.7417869892607294 >>> random.seed(5) >>> random.random() 0.6229016948897019 >>> random.random() 0.7417869892607294 >>> random.seed(123) >>> random.randint(1,100) 6 >>> random.randint(1,100) 9 >>> random.seed(123) >>> random.randint(1,100) 6 >>> random.randint(1,100) 9 >>> random.seed(321) >>> random.randint(1,100) 28 >>> random.randint(1,100) 13 2、random.getrandbits(k) 生成具有k个二进制位的随机整数。例如： >>> random.getrandbits(3) 0L >>> random.getrandbits(3) 2L >>> random.getrandbits(3) 7L >>> random.getrandbits(3) 6L >>> random.getrandbits(3) 2L 3、rangdom.randrange([start,]stop[,step]) 返回range([start,]stop[,step])之间的随机数，等价于choice(range([start,]stop[,step])，区别在于该函数并不真的创建一个range对象。例如： >>> random.randrange(5) 3 >>> random.randrange(5,20,3) 11 >>> random.randrange(5,20,5) 15 4、random.randint(start,end) 返回闭区间[start,end]之间的随机整数，类似于randrange(start,end+1)。例如： >>> [random.randint(5,20) for i in range(20)] [12, 16, 19, 17, 8, 15, 6, 13, 6, 12, 7, 7, 9, 7, 14, 20, 6, 9, 9, 7] 5、random.choice(seq) 从序列seq中随机选择一个元素并返回。例如： >>> random.choice('abcdefg') 'a' >>> random.choice('abcdefg') 'g' >>> random.choice([1,2,3,4,5,6]) 4 >>> random.choice([1,2,3,4,5,6]) 2 >>> random.choice((1,2,3,4,5,6)) 5 6、random.shuffle(seq) 将序列seq原地乱序。例如： >>> x = list(range(20)) >>> random.shuffle(x) >>> x [16, 15, 3, 12, 6, 14, 1, 2, 13, 8, 4, 9, 17, 18, 11, 7, 19, 5, 10, 0] 7、random.sample(seq, k) 从序列或集合seq中随机选择k个不同的（这里并不是指元素值）元素，以列表形式返回。例如（接上面的代码）： >>> random.sample(x,3) [18, 3, 2] >>> random.sample(x,3) [3, 17, 8] >>> random.sample(x,3) [2, 14, 7] >>> y = [1,2,2,2,1,3] >>> random.sample(y,3) [2, 2, 1] >>> random.sample(y,3) [2, 2, 1] >>> random.sample(y,3) [2, 2, 3] 8、random.random() 返回左闭右开区间[0.0,1.0)之间的浮点数。 9、random.uniform(a,b) 返回介于[a,b]或[b,a]之间的随机浮点数。例如： >>> random.uniform(3,5) 4.84352763680075 >>> random.uniform(5,3) 4.635435982260146 10、random.triangular(low,high,mode) 返回介于[low,high]之间的随机浮点数，mode用于确定数值如何分布。例如： >>> random.triangular() 0.6041766419310899 >>> random.triangular() 0.809202355489536 >>> random.triangular() 0.39545940385391254 >>> random.triangular(mode=0.1) 0.11213068022993511 >>> random.triangular(mode=0.1) 0.14202201162618033 >>> random.triangular(mode=0.1) 0.07648650142198485 >>> random.triangular(3,5,mode=4.1) 4.0060114547695695 >>> random.triangular(3,5,mode=4.1) 3.7841619928542487 >>> random.triangular(3,5,mode=3.1) 3.594104706668854 11、random.betavariate(alpha,beta) 返回[0,1]之间的符合beta分布的随机浮点数，两个参数要求大于0。例如： >>> random.betavariate(3,5) 0.4774740780821406 >>> random.betavariate(3,5) 0.3996755034928471 >>> random.betavariate(3,50) 0.058100787064147986 12、random.expovariate(lambd) 返回符合lambd分布的随机数。例如： >>> [random.expovariate(3) for i in range(10)] [0.023746839946594114, 0.4413273605121732, 0.12551353053608152, 0.013493207269662204, 0.29947366176757295, 0.05612131847508229,

0.047628127058363855, 1.3205129984044726, 0.08792536205084321, 0.09437795307155394] >>> [random.expovariate(-3) for i in range(10)] [-0.5840103217481932, -0.1429878665439176, -0.320509040220251, -0.3277959011141573, -0.4593551780229827, -0.25977304321413436, -

0.38937311888802556, -0.7204132540876763, -0.1232984045589699, -0.13544652703833246] >>> [random.expovariate(-30) for i in range(10)] [-0.032248098097416224, -0.050302433165153586, -0.014618853197399763, -0.011403100190286985, -0.108163759181155, -0.014175458942549098, -

0.017466307097120947, -0.0946695841475753, -0.051734748354947326, -0.003952288703677691] 13、random.gammavariate(alpha,beta) 返回符合gamma分布的随机数，要求两个参数大于0。例如： >>> [random.gammavariate(3,5) for i in range(10)] [12.22246084443096, 24.678533917988172, 5.486830306916827, 12.242217699498275, 27.744458573822325, 15.63044532881201, 10.310423266683404,

14.921246065682253, 5.3442532179846145, 28.23953804581197] >>> [random.gammavariate(30,5) for i in range(10)] [172.4916323997075, 213.82625932922335, 137.81402565067157, 162.89624745025762, 153.97373733808928, 131.34151959572236, 137.99405726886417,

183.36910321346838, 141.15859464845778, 138.23763089032002] 14、random.gauss(mu,sigma) 返回符合gauss分布的随机数，其中mu为平均数，sigma是标准差。例如： >>> [random.gauss(3,2) for i in range(10)] [1.7757284660473014, 5.077754226706221, 0.8824129559831824, 4.059287688438886, 3.222237914813805, 3.6594159482351074, 3.8908231956332036,

3.468488929507344, 3.1015041749618733, 4.7795188461395695] >>> [random.gauss(30,2) for i in range(10)] [31.1436036337679, 26.90501185383272, 30.43681854026509, 29.165851569198466, 30.930642293427265, 31.582678686552505, 30.744169293495503,

28.012052168706838, 29.663863892820565, 27.63818542918888] >>> [random.gauss(30,20) for i in range(10)] [4.453358574830325, 5.382067977081544, 46.03802800315538, 22.24850207514115, 48.16811096334578, 18.301937532127866, 41.88030952485087,

36.639818172662906, 57.02701135874143, 37.28867527579759] 15、random.lognormvariate(mu,sigma) 返回符合对数正态分布的随机数，mu可以为任意值，sigma必须大于0。例如： >>> [random.lognormvariate(3,2) for i in range(10)] [136.89029332157023, 500.70765648541476, 79.78703935304308, 8.32688876811877, 30.160030683008884, 48.76707958880316, 456.1243769893165,

7.878500122309458, 0.8948876344189048, 7.5364705758649] 16、random.normalvariate(mu,sigma) 返回符合正态分布的随机数，mu表示平均值，sigma表示标准差。 17、random.vonmisesvariate(mu,kappa) 返回符合von mises分布的随机数（弧度）。mu为[0,2*pi]之间的平均值，kappa表示浓度参数且比喻大于等于0。例如： >>> random.vonmisesvariate(2,2) 1.9444347927073324 >>> random.vonmisesvariate(2,3) 2.2057918593621713 >>> random.vonmisesvariate(2,30) 2.2678252533865644 >>> random.vonmisesvariate(2,0) 4.878056242725338 18、random.paretovariate(alpha) 返回符合Pareto分布的随机数，alpha为形状参数。例如： >>> random.paretovariate(3) 1.011188953140707 >>> random.paretovariate(3) 1.2772046588048263 >>> random.paretovariate(60) 1.0236529715195033 19、random.weibullvariate(alpha,beta) 返回符合Weibull分布的随机数，alpha表示比例参数，beta表示形状参数。 附：random模块中的Random和SystemRandom类还可以这样用。 >>> r = random.Random() >>> r.randint(3,100) 63 >>> r.choice('abcdefg') 'a' >>> r = random.SystemRandom() >>> r.choice('abcdefg') 'c' >>> r.choice('abcdefg') 'd' >>> r.randint(3,100) 87 >>> r.gauss(3,5) -4.09461403339122