random.seed(42) # 设置种子 random_number = random.random() print(f"Random Number with Seed: {random_number}") 这对于需要在不同运行之间获得相同随机数序列的情况非常有用。 总结 random模块为Python开发者提供了强大的随机数生成工具。从基础的随机数生成到序列操作和分布生成,该模块的功能十分全面。通...
operation to generate a number within the specified range except for specific numbersprint("\nGenerate a number in a specified range (-5, 5) except [-5, 0, 4, 3, 2]")# Call the 'generate_random' function with the specified range and excluded numbers, then print the resultprint(generat...
Generators: Objects that transform sequences of random bits from a BitGenerator into sequences of numbers that follow a specific probability distribution (such as uniform, Normal or Binomial) within a specified interval. Since Numpy version 1.17.0 the Generator can be initialized with a number of ...
Get the next random number in the range [0.0, 1.0) 取0到1直接的随机浮点数 importrandomprint(random.random()) C:\python35\python3.exe D:/pyproject/day21模块/random随机模块.py0.3105503800442595 2、randint(self, a, b) Return random integer in range [a, b], including both end points. 返...
这个就是random和range函数的合二为一了。但注意,range用法有变。 In [27]: import random In [28]: random.randrange(1,6) Out[28]:3 1. 2. 3. 4. 2.6 random.shuffle(x[,random]) 正如函数名所表示的意思,shuffle,洗牌,将一个列表中的元素打乱。
一random.random() 生成0<=n<1随机浮点数 二random.unifrom(a,b) 生成指定范围内的浮点数,包含a,b 三random.randint(a,b) 生成指定范围整数,包含a,b.其中a为下限,b为上限。 四random.randrange([start,]stop[,step]) 从序列range([start,]stop[,step])中取出一个数,等同random.choice(range([start,...
Get a random number in the range [a, b) or [a, b] depending on rounding. # 生成前开后闭区内的随机浮点数>>> random.uniform(1,8)7.370822144312884>>> random.uniform(1,8)4.466816494748985>>> random.uniform(1,8)1.8154762190957459>>> ...
看代码：>>>importrandom>>>number=5# 1>>>a=[]>>>for_inrange(number):# 2a.append(random....
Python random shuffle: Shuffle or randomize the any sequence in-place. Python random float number using uniform(): Generate random float number within a range. Generate random string and passwords in Python: Generate a random string of letters. Also, create a random password with a combination ...
df = pd.DataFrame({'A': range(100000), 'B': range(100000)}) start_time = time.time() # 正确:使用向量化运算 df['Sum_Vectorized'] = df['A'] + df['B'] end_time = time.time() print(f"向量化运算耗时: {end_time - start_time:.4f} 秒") # 耗时显著减少 ...