| """Generate the synthetic CSVs in this dataset. Every value is invented. |
| |
| Brands, model names, prices and dates are made up by a seeded random |
| generator; nothing is real, scraped or copied. Re-running this script |
| reproduces the files byte for byte: |
| |
| python make_data.py |
| """ |
|
|
| import csv |
| import random |
| from datetime import date, timedelta |
|
|
| SEED = 20260927 |
|
|
| BRANDS = ["Arvelo", "Brindlecore", "Corvane", "Duskmere", "Elmstead", "Fennix", "Glimmerly", "Halvorn"] |
| CATEGORIES = { |
| |
| "cpu": ((79, 899), ["Vex", "Torrent", "Kestrel"]), |
| "gpu": ((149, 1899), ["Auralis", "Nimbus", "Ember"]), |
| "ssd": ((39, 549), ["Drift", "Slate", "Quill"]), |
| "ram": ((29, 429), ["Pulse", "Lattice", "Ripple"]), |
| "motherboard": ((89, 699), ["Keystone", "Harbor", "Trellis"]), |
| "psu": ((49, 329), ["Volta", "Current", "Bastion"]), |
| "monitor": ((119, 1299), ["Vista", "Prism", "Horizon"]), |
| } |
| STORES = ["north-depot", "harbor-outlet", "ridge-market", "online"] |
|
|
|
|
| def hardware_prices(rng: random.Random) -> list[dict]: |
| rows = [] |
| seen = set() |
| while len(rows) < 200: |
| brand = rng.choice(BRANDS) |
| category = rng.choice(sorted(CATEGORIES)) |
| (lo, hi), stems = CATEGORIES[category] |
| model = f"{rng.choice(stems)} {rng.choice('ABCDEFGHJKLMNPRSTXZ')}{rng.randint(1, 9)}{rng.choice(['', '0', '00', '50'])}" |
| if (brand, model) in seen: |
| continue |
| seen.add((brand, model)) |
| rows.append( |
| { |
| "id": len(rows) + 1, |
| "brand": brand, |
| "model": model, |
| "category": category, |
| "price_usd": f"{rng.uniform(lo, hi):.2f}", |
| "release_year": rng.randint(2019, 2026), |
| } |
| ) |
| return rows |
|
|
|
|
| def price_observations(rng: random.Random, products: list[dict]) -> list[dict]: |
| """A second, long-shaped table: price checks of some products over time.""" |
| rows = [] |
| start = date(2026, 1, 5) |
| for n in range(120): |
| p = rng.choice(products) |
| base = float(p["price_usd"]) |
| observed = start + timedelta(days=rng.randint(0, 250)) |
| in_stock = rng.random() > 0.2 |
| rows.append( |
| { |
| "observation_id": f"obs-{n + 1:04d}", |
| "product_id": p["id"], |
| "store": rng.choice(STORES), |
| "observed_on": observed.isoformat(), |
| "price_usd": f"{base * rng.uniform(0.85, 1.12):.2f}" if in_stock else "", |
| "in_stock": "true" if in_stock else "false", |
| } |
| ) |
| rows.sort(key=lambda r: (r["observed_on"], r["observation_id"])) |
| return rows |
|
|
|
|
| def write(path: str, rows: list[dict]) -> None: |
| with open(path, "w", newline="", encoding="utf-8") as f: |
| w = csv.DictWriter(f, fieldnames=list(rows[0]), lineterminator="\n") |
| w.writeheader() |
| w.writerows(rows) |
|
|
|
|
| if __name__ == "__main__": |
| rng = random.Random(SEED) |
| products = hardware_prices(rng) |
| write("hardware_prices.csv", products) |
| write("price_observations.csv", price_observations(rng, products)) |
|
|
