Randomness and simulation · ⁨ランダム性とシミュレーション⁩

What we'll do

• Some programs need chance, like games or experiments.
• Python can make random numbers for us.
• We will also build a tiny simulation (a model of something real).

Import random

• The random tools live in a module named random.
• Write import random at the top of your program.
• Then you can use its functions, like random.randint.

import random

number = random.randint(1, 6)
print(number)

random.randint(a, b)

• random.randint(a, b) gives a whole number from a to b.
• Both ends are included: randint(1, 6) can be 1, 6, or anything between.
• Run it again and you may get a different number.

import random

for i in range(3):
    print(random.randint(1, 6))

Same numbers with a seed

• A seed sets the random start point.
• With the same seed, you get the same numbers every time.
• This is useful for testing, so results can be repeated.

import random

random.seed(42)
print(random.randint(1, 6))
print(random.randint(1, 6))

A tiny simulation

• A simulation models a real process with code.
• Here we model flipping a coin many times and counting heads.
• Running many trials helps us see the long-run pattern.

import random

random.seed(1)
heads = 0
for i in range(10):
    flip = random.randint(0, 1)   # 0 = tails, 1 = heads
    if flip == 1:
        heads = heads + 1
print(heads)

In AP CSP pseudocode

• The exam writes random numbers with RANDOM.
• RANDOM(a, b) gives a whole number from a to b, the same idea as random.randint(a, b).

roll ← RANDOM(1, 6)
DISPLAY(roll)

Common mistakes

• A simulation uses random values to model chance.
• Run it many times to see the overall pattern.

Now you try

• Some tasks check a property (like "the roll is between 1 and 6").
• Some tasks use a seed so the output is the same every time.
• Press Check answer to test your code.