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Python Random module

The Python Random module is a built-in module for generating random integers in Python. These numbers occur randomly and does not follow any rules or instructuctions. We can therefore use this module to generate random numbers, display a random item for a list or string, and so on.

The random() Function

The random.random() function gives a float number that ranges from 0.0 to 1.0. There are no parameters required for this function. This method returns the second random floating-point value within [0.0 and 1] is returned.

Code

Output:

0.3232640977876686

The randint() Function

The random.randint() function generates a random integer from the range of numbers supplied.

Code

Output:

215	

The randrange() Function

The random.randrange() function selects an item randomly from the given range defined by the start, the stop, and the step parameters. By default, the start is set to 0. Likewise, the step is set to 1 by default.

Code

Output:

4
9

The choice() Function

The random.choice() function selects an item from a non-empty series at random. In the given below program, we have defined a string, list and a set. And using the above choice() method, random element is selected.

Code

Output:

M
765
54

The shuffle() Function

The random.shuffle() function shuffles the given list randomly.

Code

Output:

[23, 43, 86, 65, 34, 23]
[65, 23, 86, 23, 34, 43]

Rock-Paper-Scissor Program using Random Module

Code

Output:

This is Javatpoint's Rock-Paper-Scissors! 
 Please Enter your choice: 
 choice 1: Rock 
 choice 2: Paper 
 choice 3: Scissors 
 Select any options from 1 - 3 : 1
 Option choosed by Machine is:  Rock 
 Wow It's a tie! 
 Want to Play again? ( yes / no ) yes
 This is Javatpoint's Rock-Paper-Scissors! 
 Please Enter your choice: 
 choice 1: Rock 
 choice 2: Paper 
 choice 3: Scissors 
 Select any options from 1 - 3 : 2
 Option choosed by Machine is:  Scissors 
 Congratulations!! You won! 
 Want to Play again? ( yes / no ) no
 Thanks for playing Rock-Paper-Scissors!

Various Functions of Random Module

Following is the list of functions available in the random module.

Function Description
seed(a=None, version=2) This function creates a new random number.
getstate() This method provides an object reflecting the generator's present state. Provide the argument to setstate() to recover the state.
setstate(state) Providing the state object resets the function's state at the time getstate() was invoked.
getrandbits(k) This function provides a Python integer having k random bits. This is important for random number production algorithms like randrange(), which can manage arbitrarily huge ranges.
randrange(start, stop[, step]) From the range, it produces a random integer.
randint(a, b) Provides an integer within a and b at random (both inclusive). If a > b, a ValueError is thrown.
choice(seq) Produce a non-empty series item at random.
shuffle(seq) Change the order.
sample(population, k) Display a list of k-size unique entries from the population series.
random() This function creates a new random number.
uniform(a, b) This method provides an object reflecting the generator's present state. Provide the argument to setstate() to recover the state.
triangular(low, high, mode) Providing the state object resets the function's state at the time getstate() was invoked.
guass(mu, sigma) With mean and standard deviation, a float number is generated randomly.
betavariate(alpha, beta) With alpha and beta, a float number is generated randomly between the range 0 and 1. - Beta Distribution
expovariate(lambda) Float number is generated by using the argument lambda. - Exponential Distribution
normalvariate(mu, sigma) With mean and standard deviation, a float number is generated randomly. - Normal Distribution
gamavariate(alpha, beta) With alpha and beta, a float number is generated randomly. - Gamma Distribution

Conclusion

To conclude, We learned about various methods that Python's random module provides us with for dealing with Integers, floating-point numbers, and other sequences like Lists, tuples, etc. We also looked at how the seed affects the pseudo - random number pattern.







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