How does `random.seed()` control the randomness in Python's `random` module?

Understanding the Role of random.seed() in Python

Python's random module provides a set of functions for generating pseudo-random numbers. To ensure that these numbers are not entirely arbitrary, Python utilizes a seed value to initialize the underlying algorithm.

Function of random.seed()

random.seed() initializes the internal state of the pseudo-random number generator (PRNG) used by the random module. It takes a single argument, which serves as the seed value.

How Seeding Works

PRNGs generate numbers based on a mathematical function applied repeatedly to a previous value. In the absence of a seed, Python initializes the PRNG with an arbitrary value.

By providing a seed, you can control the starting point of the PRNG, ensuring that it generates the same sequence of numbers each time it is invoked with the same seed. This can be useful for testing or creating reproducible experiments.

Examples

Consider the following code:

import random

random.seed(9001)
print(random.randint(1, 10))
print(random.randint(1, 10))
print(random.randint(1, 10))

This code will always produce the following output:

1
3
6

If we were to change the seed to a different value, the sequence of numbers generated would be entirely different.

Practical Applications of Seeding

While seeding is not usually necessary for general applications of randomness, it is invaluable in the following scenarios:

• Testing: Seeding allows for deterministic testing of code that relies on random numbers.
• Reproducibility: Experiments or simulations can be reproduced by using the same seed across multiple runs.
• Unbiased Data: Seeding can help eliminate bias from random samples when the default random initialization is not suitable.

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