python3-Notes-e-001-Library-random number

Import Random# random Number Module lists = [1,2,3,4,5]DefDemo():# Generate [0, 100] random integer num = random.randint (0,) Print (num)# Generate [0, 100) random floating-point number Fnum = Random.uniform (0,) Print (Fnum)# random Get element Elem = Random.choice (lists) print (Elem)# Scrambled Order random.shuffle (lists) print (lists)DefFuns():# seed (A=none, version=2)//Initial change generator random number Random.seed () random.getstate ()# Get Generator Internal state random.setstate (Random.getstate ())# Set Generator internal state# get random number num = Random.getrandbits (8)# get an X-bit (bit) random integer# Randrange (stop)/Randrange (Start, stop[, step])//generate random integer num = random.randrange (0,100,2)# [0,100] generated random integer +2# Randint (A, b) = = Randrange (A, B + 1)//[A, b] num = Random.randint (0,1) Fnum = Random.random ()# get floating-point random number [0.0, 1.0] Fnum = Random.uniform (1,2)# Gets the floating-point random number in the specified range [1.0, 2.0]# Triangular (low, high, mode)//Get random floating-point number, lower edge (default 0), HI boundary (default 1), Mode (midpoint of default boundary) Fnum = Random.triangular (0,1,1.5)# betavariate (alpha, Beta)//beta distribution, [0.0, 1.0] Fnum = Random.betavariate (1,1)# expovariate (LAMBD)//exponential distribution, LAMBD return integer, value [0, +∞]; LANBD return negative, value [-∞, 0] fnum = random.expovariate ((Lambda arg1, Arg2:arg1 + arg2) (1,2))# The smaller the return value of LAMBD, the greater the gain# gammavariate (alpha, Beta)//Gamma Distribution Fnum = Random.gammavariate (1,1)# Gauss (Mu, sigma)//Gaussian distribution mu: mean, sigma: standard deviation fnum = Random.gauss (1,1)# lognormvariate (MU, sigma)//logarithmic normal distribution, get the average distribution of mu and standard deviation sigma; MU: Any value, sigma:>0. Fnum = Random.lognormvariate (1,1)# normalvariate (MU, sigma)//Normal distribution, mu is mean, sigma is standard deviation fnum = Random.normalvariate (1, 1) # vonmisesvariate (Mu, kappa)/ /Vom Mises distribution of random numbers. MU: Average angle (radians [0, 2*pi]), Kappa: concentration degree >=0 fnum = random.vonmisesvariate (1, 1) # paretovariate (Alpha)//Pareto distribution, alpha: Shape Fnum = random.paretovariate (1) # weibullvariate (alpha, Beta)//Weber distribution, Alpha: Zoom, Beta: shape fnum = random . Weibullvariate (1, 1) Elem = Random.choice (lists) # the random element in a non-empty sequence, the sequence is empty throw indexerror Elems = random.sample (lists, 3) # randomly get 3 elements from the list, range > list size, throw valueerror # Shuffle random.shuffle (lists) # scrambled sequence             

Python3-notes-e-001-Library-random number randomly