Python random Introduction














































Python random Introduction



INTRODUCTION TO PYTHON random MODULE

 Python has a built-in random module that implements pseudo-random number generators for various distributions.This module contains methods that let us directly solve many different programming issues where randomness comes into play.
The random module is based on Marsenne Twister - a very popular algorithm, which is the default pseudo-random number generator not only for Python, but also for many other popular software systems such as Microsoft Excel, MATLAB, R, or PHP. Its important advantages include permissive licensing, random-likeness confirmed by many statistical tests, and relatively high speed compared to other pseudo random number generators.

What are Pseudo Random Number Generators ?

 In many programming cases, all we need are sets of numbers that seem random. This kind of data can be derived from pseudo-random number generators. These are algorithms, that use a tiny portion of information (called a seed) and then apply complicated mathematical formulas in order to generate deterministic sets of numbers resembling truly random sets.

How to use a random module?
 You need to import the random module in your program, and you are ready to use this module. Use the following statement to import the random module in your code.

import random

Code for some random module functions use:

Output:



random Module Functions :-

  • random() is the most basic function of the random module.
  • Almost all functions of the random module depend on the basic function random().
  • random() return the next random floating-point number in the range [0.0, 1.0).

   --> random module has a set of methods which are defined below :-


           Bookkeeping functions :


 
random.seed()

Initialize the random number generator.


random.getstate()

Return an object capturing the current internal state of the generator. This object can be passed to setstate() to restore the state.

random.setstate(state)

state should have been obtained from a previous call to getstate(), and setstate() restores the internal state of the generator to what it was at the time getstate() was called.

random.getrandbits(k)

Returns a Python integer with k random bits.

 


Functions for Integers :

random.randrange(stop)
random.randrange(startstopstep)
   Returns a randomly selected element from the given range.
random.randint(ab)

Return a random integer N such that a <= N <= b

    

Functions for Sequences :


random.choice(seq)

Return a random element from the non-empty sequence seq.  seq is empty will raises to index Error.

random.choices(seq,k=1)

Return a k sized list of elements chosen from the seq. If the seq is empty, raises index Error.

random.shuffle(x[random])

Take a sequence and returns the sequence in random order.

random.sample(seqk)

Return a k length list of unique elements chosen from the seq .


Real-valued distributions :

The following functions generate specific real-valued distributions. Function parameters are named after the corresponding variables in the distributions equation, as used in common mathematical practice; most of these equations can be found in any statistics text.

random.random()

Return the next random floating point number in the range [0.0, 1.0).

random.uniform(ab)

Return a random floating point number N such that a <= N <= b for a <= b and b <= N <= a for b < a.


random.triangular(lowhighmode)

Return a random floating point number N such that low <= N <= high and with the specified mode between those bounds. The low and high bounds default to zero and one. The mode argument defaults to the midpoint between the bounds, giving a symmetric distribution.

random.betavariate(alphabeta)

Returns a random float number between 0 and 1.Conditions on the parameters are alpha > 0 and beta > 0

random.expovariate(lmbd)

Exponential distribution. lmbd is 1.0 divided by the desired mean. It should be nonzero. (The parameter would be called lambda, but that is a reserved word in Python.) Returned values range from 0 to positive infinity if lmbd is positive, and from negative infinity to 0 if lmbd is negative.

random.gammavariate(alphabeta)

Gamma distribution. (Not the gamma function!) Conditions on the parameters are alpha > 0 and beta > 0.

random.gauss(musigma)

Gaussian distribution. mu is the mean, and sigma is the standard deviation. This is slightly faster than the normalvariate() function defined below.

random.lognormvariate(musigma)

Log normal distribution. If you take the natural logarithm of this distribution, you get a normal distribution with mean mu and standard deviation sigmamu can have any value, and sigma must be greater than zero.

random.normalvariate(musigma)

Normal distribution. mu is the mean, and sigma is the standard deviation.

random.vonmisesvariate(mukappa)

mu is the mean angle, expressed in radians between 0 and 2*pi, and kappa is the concentration parameter, which must be greater than or equal to zero. If kappa is equal to zero, this distribution reduces to a uniform random angle over the range 0 to 2*pi.

random.paretovariate(alpha)

Pareto distribution. alpha is the shape parameter.

random.weibullvariate(alphabeta)

Weibull distribution. alpha is the scale parameter and beta is the shape parameter.


Alternative Generator :


class random.Random([seed])

Class that implements the default pseudo-random number generator used by the random module.

class random.SystemRandom([seed])

Class that uses the os.urandom() function for generating random numbers from sources provided by the operating system. Not available on all systems. Does not rely on software state, and sequences are not reproducible. Accordingly, the seed() method has no effect and is ignored. The getstate() and setstate() methods raise NotImplementedError if called.



***Go and visit my next articles to learn more about every random module functions in details***

More Articles of Vishal Lodhi:

Name Views Likes
Python string zfill 137 0
Python string swapcase 108 0
Python string title 109 0
Python string startswith 118 0
Python string replace 159 0
Python string translate 99 0
Python string rpartition 96 0
Python string partition 92 0
Python string splitlines 138 0
Python string rsplit 90 0
Python string split 90 0
Python string rindex 99 0
Python string rfind 121 0
Python string upper 95 0
Python string lower 97 0
Python string maketrans 104 0
Python string strip 95 0
Python string rstrip 115 0
Python string lstrip 96 0
Python string rjust 114 0
Python string ljust 113 0
Python string len 97 0
Python string join 92 0
Python string casefold 97 0
Python string isprintable 102 0
Python string encode 95 0
Python string isdecimal 101 0
Python string isidentifier 93 0
Python string isupper 96 0
Python string istitle 94 0
Python string isspace 115 0
Python string isnumeric 118 0
Python string isdigit 99 0
Python string islower 100 0
Python string isalpha 115 0
Python string isalnum 114 0
Python string index 103 0
Python string find 109 0
Python string expandtabs() 91 0
Python string endswith 109 0
Python string count 105 0
Python string capitalize 92 0
Python string center 96 0
Python string Introduction 115 0
Python string Template 160 0
Python string Formatter 169 0
Python string printable 246 0
Python string whitespace 163 0
Python string punctuation 119 0
Python string octdigits 104 0
Python string hexdigits 125 0
Python string digits 91 0
Python string ascii_letters 426 0
Python string ascii_lowercase 204 0
Python string ascii_uppercase 198 0
How to Create Download Manager in Python 772 0
Python random weibullvariate 185 0
python random paretovariate 151 0
Python random vonmisesvariate 198 0
Python random normalvariate 228 0
Python random lognormvariate 134 0
Python random gauss 142 0
Python random gammavariate 158 0
Python random expovariate 232 0
Python random betavariate 235 0
Python random triangular 128 0
Python random uniform 165 0
Python random random 129 0
Python random sample 213 0
Python random shuffle 198 0
Python random choices 278 0
Python random choice 180 0
Python random randint 165 0
Python random randrange 259 0
Python random getrandbits 167 0
Python random setstate 162 0
Python random getstate 207 0
Python random seed 186 1
Python random Introduction 194 0

Comments