I Built a "Fastest Pizza Near Me" Tool with the UberEats API and Python

#webscraping #python #tutorial #api

"Which pizza place near me is actually good and fast tonight?"

UberEats doesn't really answer that. It sorts by relevance, promotions, and whatever the algorithm feels like — not by "best combination of rating and delivery speed," which is what I actually want at 8pm on a Tuesday.

So I built a tiny tool that answers it properly, using the UberEats Stores Search by Location and Keyword actor on Apify and about 40 lines of Python.

The idea

Pull every store matching a keyword at a given address — name, rating, estimated delivery time — then rank them myself instead of trusting the app's default order. A place with a 4.8 rating and 45-minute delivery isn't necessarily better than a 4.5-rated spot that shows up in 20.

Walkthrough

Install the client:

pip install apify-client

Fetch the data:

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run_input = {
    "locations": ["85 Marsh St, Newark, NJ 07114, USA"],
    "search_keyword": "pizza",
    "max_search_results": 30,
}

run = client.actor("datacach/ubereats-stores-search-by-location-and-keyword").call(run_input=run_input)
items = list(client.dataset(run["defaultDatasetId"]).iterate_items())

Each store comes back with estimated_time_to_delivery as a string like "20 min". Parse it and score each place — rating matters, but delivery time should pull the score down the longer it takes:

import re

def parse_minutes(text):
    match = re.search(r"\d+", text or "")
    return int(match.group()) if match else 60  # unknown time = penalize heavily

def score(store):
    rating = store.get("rating") or 0
    minutes = parse_minutes(store.get("estimated_time_to_delivery"))
    return rating - 0.03 * minutes  # tune this weight to taste

ranked = sorted(items, key=score, reverse=True)

for store in ranked[:5]:
    print(f"{store['name']:<30} ★{store.get('rating', '?'):<5} {store.get('estimated_time_to_delivery', '?')}")

That's it — a ranked top 5 that actually balances "good" against "fast," instead of whatever order the app decided to show you.

Make it a CLI

Wrap it so you can run it for any address/keyword combo:

import sys

if __name__ == "__main__":
    address, keyword = sys.argv[1], sys.argv[2]
    # ...call the actor with {"locations": [address], "search_keyword": keyword}...

python fastest_near_me.py "123 Main St, Newark, NJ, USA" "sushi"

Ideas to extend it

Compare cuisines: run it for "pizza", "sushi", and "burgers" at the same address and see what's actually fastest tonight across the board.
Compare neighborhoods: run the same keyword across a few addresses to find which part of town has the best delivery options.
Tune the score: weight rating_count in too — a 4.8 from 3 reviews is less trustworthy than a 4.5 from 500.