Kishan Kaushik - Random Module

1. Presentation on – Conversational Campus Assistant
for Atal University using RAG and local LLMs.
Department of Computer Science and
Applications
Atal Bihari Vajpayee Vishwavidyalaya, Koni, Bilaspur,
Chhattisgarh
Presented to – Dr. H.S. Hota
Associate Professor, University
Teaching Department
Presented by – Kishan Kumar
Kaushik, Pravin Bhargaw, MCA 3rd
Semester, Computer Science.
Roll No. – 24, 29
Session – 2025-26

2. Table of contents: Load balancing
● What is load Balancing ?
● Goals of Load Balancing.
● Types of Load Balancing:
○ Static Load Balancing.
○ Dynamic Load Balancing.
● Strategies of Load Balancing:
○ Centralised.
○ Distributed.

3. Table of contents: Virtualization
● Virtualization definition.
● Virtualization Architecture.
● Benefits of Virtualization.
● Virtualization in Cloud Computing.
● Hypervisor.
● Server without virtualization.
● Server with virtualization.
● Hypervisor types.
● Hypervisor implementation approaches.
● Popular hypervisors.

4. • A module is a simple file written in python code
which can be used for defining various types of
classes, variables as well as other codes.
• We can reference any python file as a module and
use it.
• A file containing Python code, for example:
kishan.py , is called a module, and the name of the
module will be kishan.
What is module in
python?

5. • Example: Let’s create a simple module named
kishan, we will save it as kishan.py
Continue…
=

6. • The random module in Python is a built-in library
that allows us to generate random numbers and
perform random type of operations in the programs.
• This module can be used to perform random actions
such as generating random numbers, printing random
a value for a list etc.
What is random module?

7. Importing Random Module:
Following syntax is used for importing the
random module:
import random
random.<function_name>
Example:

8. Algorithm behind random module:
• In Python, random module generates the numbers using the Mersenne Twister
algorithm, which is a pseudorandom number generator (PRNG).
• It is called "Mersenne Twister" because its total length (the number of random
numbers it can produce before repeating) is based on Mersenne primes.
• The random numbers generated by Python's random module are floating-point
numbers with 53 bits of precision.
• The period of the Mersenne Twister is 2² ³ − 1, which is an extremely large
⁰⁰ ⁷
number (approximately 10 ¹¹).
⁶⁰

9. Continued...
Working of Mersenne Twister:
• State Initialization: It maintains an internal state array of 624 integers (each 32
bits).
• Twisting Step: At each step, the generator updates its internal state by
combining values from the state array using bitwise operations.
• Output Transformation: The output is refined with a tempering process to
improve randomness.

10. Built-in methods in random module:
• To get the list of available methods which
are built in inside the random module, we
can write, use the help() function.

11. Continued...
• Following are the most commonly used built-in methods in random module within python:
Method Description
random() Returns a random float number between 0 and 1
randint() Returns a random number between the given
range.
randrange() Returns a random number between the given
range with step value.
getrandbits() Returns a number representing the random bits.
getstate() Get the current internal state of the random
number generator.
setstate() Set the current internal state of the random
number generator.
choice() Returns a random element from the given
sequence.
choices() Returns a list with a random selection from the
given sequence.
shuffle() Takes a sequence and returns the sequence in a
random order.
sample() Returns a given sample of a sequence.
seed() Initialize the random number generator.
uniform() Returns a random float number between two
given parameters.

12. Continued…
random(): Returns a random float number between 0 and 1

13. Continued…
randint(): Returns a random number between the given range.

14. Continued…
randrange(): Returns a random number between the given range
with step value.

15. Continued…
getrandbits(): Returns a number representing the random bits.

16. Continued…
getstate(): Get the current internal state of the random number
generator.
setstate(): set the current internal state of the random number
generator.

17. Continued…
choice(): Returns a random element from the given
sequence.

18. Continued…
choices(): Returns a list with a random selection from the
given sequence.

19. Continued…
shuffle(): Takes a sequence and returns the sequence in a
random order.

20. Continued…
sample(): Returns a given sample of a sequence.

21. Continued…
seed(): Initialize the random number generator.

22. Continued…
uniform(): Returns a random float number between
two given parameters.

23. Problems with random module
algorithm:
The Mersenne Twister is used for random module as it is efficient and
produces numbers that pass many statistical tests for randomness. It is
well-suited for simulations, games, and other applications that require
high-quality random numbers.
However, it's not suitable for cryptography because it's not designed to
be secure.

24. References:
⮚ https://www.w3schools.com/python/module_random.asp
⮚ https://www.geeksforgeeks.org/python-os-urandom-method/
⮚ https://docs.python.org/3/library/random.html
⮚ https://en.wikipedia.org/wiki/Mersenne_Twister
⮚ https://www.youtube.com/watch?v=qI4VUVUOA5I
⮚ https://stackoverflow.com/questions/69530965/is-os-urandom-fu
ncion-in-python-really-a-random-function

25. Thank You!
Any questions?