ABC School has allotted unique token IDs from (1 to 600) to all the parents for facilitating a lucky draw on t

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Q. ABC School has allotted unique token IDs from (1 to 600) to all the parents for facilitating a lucky draw on the day of their Annual day function. The winner would receive a special prize. Write a program using Python that helps to automate the task.(Hint: use random module)

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Concept understanding — Random Number Generation

Random Number Generation: From Intuition to Precision

Imagine you're playing a board game with a single die. Before every move, you roll it. You don't know which face will come up — it could be 1, 2, 3, 4, 5, or 6. But you do know one thing: over many rolls, each number appears roughly the same number of times. That's the core idea behind random number generation: producing a sequence of numbers where each outcome is unpredictable individually, yet the overall pattern follows a known distribution.

A random number generator (RNG) is a process — either physical or computational — that produces such a sequence. In the physical world, rolling a die, flipping a coin, or measuring radioactive decay are genuine sources of randomness. In computing, we usually simulate randomness using algorithms, because true randomness is hard to capture and slow to produce.

The Intuition: What Makes a Number "Random"?

A single number, say 42, is not random or non-random by itself. Randomness is a property of the process that produced it, not the number itself. So when we talk about random number generation, we're really talking about generating a sequence of numbers that satisfies two key properties:

Unpredictability: Given the entire history of numbers generated so far, you cannot predict the next number with any certainty better than pure chance.
Uniformity (for a uniform generator): Over a long run, each possible number appears with equal frequency. For a fair six-sided die, each face appears about 1/6 of the time.

If you roll a die 60 times and get 60 sixes, the process is still "random" in principle — but it's almost certainly a loaded die. A good RNG must pass statistical tests that check for both unpredictability and uniformity.

The Precise Statement

A random number generator is a deterministic or non-deterministic algorithm or physical process that produces a sequence of numbers $X_1, X_2, X_3, \dots$ such that:

Each $X_i$ is drawn from a specified probability distribution (most commonly the uniform distribution on $[0,1)$ or $\{0,1,\dots,m-1\}$ ).
The sequence is independent and identically distributed (i.i.d.) — meaning the value of $X_i$ does not depend on any previous $X_j$ , and all $X_i$ share the same distribution.
For a pseudo-random number generator (PRNG), the sequence is actually deterministic: given an initial seed value, the entire sequence is fixed. But the sequence appears random to any statistical test that does not know the seed.

\{0,1,\dots,m-1\}

How Computers Generate Random Numbers (Pseudo-Randomness)

True randomness is expensive. Instead, computers use pseudo-random number generators — deterministic algorithms that produce sequences that look random. The most famous family is the linear congruential generator (LCG):

X_{n+1} = (a \cdot X_n + c) \mod m

where $a$ , $c$ , and $m$ are carefully chosen constants, and $X_0$ is the seed. For example, the classic rand() in C often used $a = 1103515245$ , $c = 12345$ , $m = 2^{31}$ . …

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Use random.randint(1, 600) — it returns a random integer with both end-points included, so every token ID from 1 to 600 has an equal chance.

import random

def luckyDraw():
    winner = random.randint(1, 600)
    print("Lucky draw winner - Token ID:", winner)

luckyDraw()

Sample output (changes every run):

Lucky draw winner - Token ID: 437 …

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A fair lucky draw is exactly what pseudo-random number generation is for: random.randint(1, 600) returns each of the 600 token IDs with equal probability, both end-points included.

The idea — random number generation. Python's built-in random module produces pseudo-random numbers. For a lucky draw we need one integer in a closed range [1, 600], and random.randint(a, b) is the right tool because it includes both a and b — every parent's token has a chance.

import random

def luckyDraw():
    """Pick one winning token ID from 1 to 600 (both inclusive)."""
    winner = random.randint(1, 600)
    print("Lucky draw winner - Token ID:", winner)

luckyDraw()

Key lines

import random — brings in the random-number module.
random.randint(1, 600) — returns a random integer N such that 1 <= N <= 600.
Wrapping the logic in the user-defined function luckyDraw() means the school can re-run the draw (e.g. for a second prize) just by calling the function again. …

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Q.ABC School has allotted unique token IDs from (1 to 600) to all the parents for facilitating a lucky draw on the day of their Annual day function. The winner would receive a special prize. Write a program using Python that helps to automate the task.(Hint: use random module)

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