"Python Module Learning" 3, Random module

Reference: 1, official website; 2, others '

Here's how to do the Random module:

1Random.seed (A=none, version=2)#Initializes a pseudo-random number generator. If a or a=none is not provided, the system time is used as the seed. If a is an integer, it is used as a seed. 2Random.getstate ()#an object that returns the internal state of a current generator3Random.setstate (state)#Pass in a state object previously obtained using the GetState method, allowing the generator to revert to this state. 4Random.getrandbits (k)#returns an integer between range (0,2**k), equivalent to Randrange (0,2**k)5Random.randrange (STOP)#returns an integer between range (0,stop)6Random.randrange (Start, stop[, step])#returns an integer between range (start,stop), plus step, similar to Range (0,10,2)7Random.randint (A, B)#returns an integer between range (a,b+1) that is equivalent to Hugin range (a,b+1)8Random.choice (seq)#randomly selects an element from a non-empty sequence seq. If seq is empty, the indexerror exception pops up. 9Random.choices (population, Weights=none, *, Cum_weights=none, k=1)#version 3.6 is new. Randomly extracting k elements from a population cluster (repeatable). Weights is a list of relative weights, cum_weights is a cumulative weight, and two parameters cannot be present at the same time. TenRandom.shuffle (x[, Random])#randomly disrupts the order of the elements within the sequence x. Only for mutable sequences, for immutable sequences, use the sample () method below.  OneRandom.sample (population, K)#randomly extracting k non-repeating elements from a population sample or collection to form a new sequence. Often used for non-repeating random sampling. Returns a new sequence that does not break the original sequence. To randomly extract a certain number of integers from an integer interval, use a similar approach to sample (range (10000000), k=60), which is very efficient and space-saving. If k is greater than the length of population, the valueerror exception pops up.  ARandom.random ()#returns a floating-point number in the [0.0, 1.0) interval between left and right open -Random.uniform (A, B)#returns a floating-point number between A and B. If a>b, it is a floating-point number between B and a. Both A and b here are likely to appear in the results.  -Random.triangular (Low, high, mode)#returns the random number of the triangle distribution of a low <= N <=high. The parameter mode indicates the location of the majority occurrence.  theRandom.betavariate (alpha, Beta)#Beta distribution. The returned result is between 0~1 -Random.expovariate (LAMBD)#Exponential distribution -Random.gammavariate (alpha, Beta)#Gamma Distribution -Random.gauss (Mu, sigma)#Gaussian distribution +Random.lognormvariate (Mu, sigma)#Logarithmic normal distribution -Random.normalvariate (Mu, sigma)#Normal Distribution +Random.vonmisesvariate (Mu, kappa)#Kappa Distribution ARandom.paretovariate (Alpha)#Pareto Distribution atRandom.weibullvariate (alpha, Beta)#Weibull Distribution

Instance:

Basic Examples:

>>> Random ()#Random floating point: 0.0 <= x < 1.00.37444887175646646>>> Uniform (2.5, 10.0)#Random floating point: 2.5 <= x < 10.03.1800146073117523>>> Randrange (10)#An integer of 0-9:7>>> randrange (0, 101, 2)#0-100 of even26>>> Choice (['win','lose','Draw'])#randomly select an element from a sequence'Draw'>>> deck ='ace three four'. Split ()>>> Shuffle (deck)#Shuffle the sequence to change the original sequence>>>deck[' Four',' Both','Ace','three']>>> sample ([Ten, +, +, +], k=4)#extracts a specified number of samples without altering the original sequence and generates a new sequence[40, 10, 50, 30]>>>#6 rotations red-black green roulette (with weighted repeatable sampling), without destroying the original sequence, weight[18,18,2]>>> Choices (['Red','Black','Green'], [2], k=6)['Red','Green','Black','Black','Red','Black']>>>#Texas hold ' em calculation probability deal cards without replacement from a deck of playing cards>>>#and determine the proportion of cards with a ten-value>>>#(a ten, Jack, Queen, or king).>>> deck = collections. Counter (tens=16, low_cards=36)>>> seen = sample (List (Deck.elements ()), k=20)>>> Seen.count ('Tens')/200.15>>>#simulation probability estimate the probability of getting 5 or more heads from 7 spins>>>#of a biased coin that settles on heads 60% of the time. ' The probability of H ' is 0.6, and the probability of "T" is 1-0.6>>> trial =Lambda: Choices ('HT', cum_weights= (0.60, 1.00), k=7). Count ('H') >= 5>>> sum (Trial () forIinchRange (10000))/100000.4169>>>#probability of the median of 5 samples being in middle and quartiles>>> trial =Lambda: 2500 <= Sorted (choices (range (10000), k=5)) [2] < 7500>>> sum (Trial () forIinchRange (10000))/100000.7958

>>> fromStatisticsImportMean>>> fromRandomImportChoices>>> data=1,2,4,4,10>>> means=Sorted (mean (choices (k=5) for i in range (20 ) # mean is the average  >>> print ( f ' The sample mean of {mean (data):. 1f} has a 90% c Onfidence ' f ' interval from {means[1]:.1f} to Span class= "si" >{means[-2]:.1f} ' ) # the F usage here   

Here is a program that generates a random 4-bit verification code with a A-Z and a number 0-9 in uppercase letters

1 ImportRandom2  3Checkcode ="'4  forIinchRange (4):5Current = Random.randrange (0,4)6     ifCurrent! =I:7temp = Chr (Random.randint (65,90))8     Else:9temp = Random.randint (0,9)TenCheckcode + =Str (temp) One Print(Checkcode)

The following is the code that generates a random sequence of alphanumeric numbers of a specified length:

Importrandom, Stringdefgen_random_string (length):#the number of numbers is randomly generatedNum_of_numeric = Random.randint (1,length-1)    #All that's left is letters.Num_of_letter = Length-Num_of_numeric#randomly generate numbersNumerics = [Random.choice (string.digits) forIinchrange (num_of_numeric)]#randomly generated lettersLetters = [Random.choice (string.ascii_letters) forIinchrange (num_of_letter)]#Combine bothAll_chars = Numerics +Letters#Shufflerandom.shuffle (all_chars)#Generate final Stringresult ="'. join ([i forIinchAll_chars]) returnresultif __name__=='__main__':    Print(Gen_random_string (64))

"Python Module Learning" 3, Random module