No. 1 :Help on method betavariate in module random:betavariate(alpha, beta) method of random.Random instanceBeta distribution.Conditions on the parameters are alpha > 0 and beta > 0.Returned values range between 0 and 1.No. 2 :Help on method choice in module random:choice(seq) method of ...
range()函数一般结合for循环使用,例如遍历一个列表时,如果要通过列表的下标来打印每个元素,则可以通过range()函数实现 >>> nums = ["a","b","c","d","e"]>>>foriinrange(len(nums)):print(nums[i]) a b c d e random模块 当需要生成随机数或者从一个列表中随机取一条或多条数据时,会使用到ra...
import random price=random.randint(1000,1500) #生成范围1000-1500之间的随机整数 print('今日竞猜的商品为小米扫地机器人：价格在【1000-1500】之间：')guess=int(input('输入竞猜价格'))if guess>price:print('价格猜大了')elif guess<price:print('价格猜小了')else:print('恭喜你，猜对了')print('真实...
import random import string cod_str = string.ascii_letters+ string.digits print(cod_str) def gen_code(len=4): return ''.join(random.sample(cod_str,len)) random.sample print( [gen_code(len=6) for i in range(1000)]) 执行结果: /home/kiosk/PycharmProjects/westos5/venv/bin/python /ho...
for i in range(5): print(i,end='') print() for i in range(0,5): print(i) print('你好') import random suiji=random.randint(0,2) #包括0,1,2 for i in range(0,10,3): #这里的3指的是步长,只要我们理解为+3就可以了 print(i)发布...
random.seed（）（模块级种子）：设置整个随机模块的种子值。import random random.seed(seed_value)ran...
random.randint() 语法如下: random.randint(a, b) 语法说明: 该语句相当于random.randint(a, b+1) 返回随机整数 N 满足 a <= N <= b 示例如下: import random for i in range(5): print(random.randint(10,20)) ### 12 15 10 13 13...
>>> random.randint(10,15)12 >>> random.randrange(10,20,3)#10到20,步长为310 >>> random.randrange(10,20,3)19 >>> random.randrange(10,20,3)10 >>> random.randrange(10,20,3)16 >>> random.choice(list(range(10,20,3))) #同上,随机选择list中的一个元素返回19 ...
random.randint(1,100) for i in range(10)#1到100内取随机数放入range(10)中,返回range(10) 1. AI检测代码解析 import random; import numpy as np; x = np.random.rand(); y = np.random.rand(4,4); print(x,type(x)) print(y,type(y)) ...
values # 切分数据集为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 使用CountVectorizer将文本转换为向量 vectorizer = CountVectorizer() X_train_transformed = vectorizer.fit_transform(X_train) X_test_transformed = vectorizer....