How does `random.seed()` ensure the same sequence of random numbers in Python?

Understanding the Role of random.seed() in Python

random.seed(), a crucial function in Python's random number generation, has raised some confusion. To clarify its role, let's delve into an example:

import random
random.seed(9001)
print(random.randint(1, 10))  # 1
print(random.randint(1, 10))  # 3
print(random.randint(1, 10))  # 6
print(random.randint(1, 10))  # 6
print(random.randint(1, 10))  # 7

This code consistently produces the same sequence of random numbers because random.seed() sets the initial value for the pseudorandom number generator.

Pseudorandom Number Generators

Pseudorandom number generators rely on a previous value to generate subsequent numbers. However, they need an initial value to start the process. This is where random.seed() comes in.

The Function of random.seed()

random.seed() initializes the random number generator by providing an integer as its parameter. This integer serves as the seed, which determines the sequence of numbers that will be generated.

Reproducibility

Using the same seed value each time ensures that the same sequence of random numbers is generated. This is useful for situations where reproducibility is desired, such as testing and debugging.

Applications

Seeding random number generators is often done during program initialization. It allows users to specify a specific seed to control the randomness and maintain consistency across different executions. For instance, the current time can be used as a seed to generate unique sequences each time.

By understanding the role of random.seed() in setting the initial value for pseudorandom number generators, developers can harness the power of randomness while maintaining control over the sequences generated.

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