random.seed() in Python

Python's random.seed() function is a crucial method to initialize the pseudo-random number generator. This allows for the generation of reproducible random numbers, particularly useful in simulations, testing, and scenarios where consistent results are required.

𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄 𝗼𝗳 𝗿𝗮𝗻𝗱𝗼𝗺.𝘀𝗲𝗲𝗱()
𝗣𝘂𝗿𝗽𝗼𝘀𝗲: The primary purpose of random.seed() is to set the starting point for generating random numbers. By providing a specific seed value, you ensure the sequence of random numbers generated will be the same each time the code runs.
𝗗𝗲𝗳𝗮𝘂𝗹𝘁 𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿: If no seed value is provided, Python uses the current system time or an OS-specific source of randomness to initialize the generator, resulting in different sequences on each execution.
Syntax
The syntax for using random.seed() is as follows:

random.seed(a=None, version=2)

𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿𝘀:
𝗮: This is an optional parameter that can be an integer, float, string, bytes, or byte array. If provided, it sets the seed value. If None, the generator uses the current system time.
𝘃𝗲𝗿𝘀𝗶𝗼𝗻: This is optional and specifies converting the seed into an integer. The default value is 2, which is compatible with Python 3.


import random

# random.seed(a=None, version=2) 
# By default, a random number takes system time as a seed.

print("First set of random numbers")
for i in range(10):
    print(random.randint(1,100), end=' ')

print("\n\nsecond set of random numbers")
for i in range(10):
    print(random.randint(1,100), end=' ')


Output
First set of random numbers
84 21 5 67 63 42 10 32 96 47 

second set of random numbers
6 54 18 78 46 49 54 37 87 34 


import random
random.seed("codingbattle")
print("First set of random numbers")
for i in range(10):
    print(random.randint(1,100), end=' ')

random.seed("codingbattle")
print("\n\nsecond set of random numbers")
for i in range(10):
    print(random.randint(1,100), end=' ')

Output
First set of random numbers
16 15 67 23 5 100 5 93 37 19 

second set of random numbers
16 15 67 23 5 100 5 93 37 19 

𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝗰𝗲 𝗼𝗳 𝗨𝘀𝗶𝗻𝗴 𝗦𝗲𝗲𝗱𝘀
Using seeds in random number generation is essential for:
𝗥𝗲𝗽𝗿𝗼𝗱𝘂𝗰𝗶𝗯𝗶𝗹𝗶𝘁𝘆: Ensuring that experiments or simulations yield consistent results across multiple runs.
𝗧𝗲𝘀𝘁𝗶𝗻𝗴: Allowing developers to test algorithms with predictable inputs.

By controlling the randomness through seeding, developers can effectively manage and utilize randomness in their applications while maintaining consistency and reliability in their results.