How to Collect Food Delivery Data with Proxies
Food delivery platforms serve hyper-local content — menus, prices, and availability change based on delivery address. Geo-targeted proxy infrastructure enables market research, competitive analysis, and pricing intelligence across delivery zones.
Disclaimer: Review each platform's Terms of Service. This guide covers proxy configuration for technical implementation. Ensure compliance with applicable terms and laws.
Why Food Delivery Data Needs Geo-Proxies
Food delivery platforms are location-driven: - Different menus by delivery zone - Variable pricing including surge pricing by area - Restaurant availability changes by address - Delivery fees vary by distance and demand
Location-Based Data Collection
import httpx
import time
import random
from dataclasses import dataclass
@dataclass(frozen=True)
class DeliveryZoneData:
platform: str
location: str
restaurant_count: int
avg_delivery_fee: str
avg_delivery_time: str
collected_at: str
def collect_zone_data(
location: str,
proxy: str,
platform_url: str,
) -> DeliveryZoneData:
"""Collect delivery platform data for a specific location."""
from datetime import datetime
time.sleep(random.uniform(5.0, 10.0))
with httpx.Client(proxy=proxy, timeout=30, follow_redirects=True) as client:
resp = client.get(platform_url, headers={
"User-Agent": "Mozilla/5.0 (iPhone; CPU iPhone OS 17_0 like Mac OS X) AppleWebKit/605.1.15",
"Accept": "text/html,application/xhtml+xml",
"Accept-Encoding": "gzip, deflate, br",
})
# Extract delivery zone data from response
return DeliveryZoneData(
platform="",
location=location,
restaurant_count=0,
avg_delivery_fee="",
avg_delivery_time="",
collected_at=datetime.utcnow().isoformat(),
)Menu Price Tracking
@dataclass(frozen=True)
class MenuPrice:
restaurant: str
item_name: str
price: str
platform: str
location: str
collected_at: str
def track_menu_prices(
restaurant_url: str,
platform: str,
proxy: str,
) -> list[MenuPrice]:
"""Track menu item prices for a restaurant."""
from datetime import datetime
time.sleep(random.uniform(3.0, 7.0))
with httpx.Client(proxy=proxy, timeout=30) as client:
resp = client.get(restaurant_url, headers={
"User-Agent": "Mozilla/5.0 (iPhone; CPU iPhone OS 17_0 like Mac OS X) AppleWebKit/605.1.15",
"Accept": "application/json,text/html",
"Accept-Encoding": "gzip, deflate, br",
})
# Parse menu items and prices
return []Multi-Market Comparison
DELIVERY_MARKETS = [
{"city": "New York", "state": "NY", "zip": "10001"},
{"city": "Los Angeles", "state": "CA", "zip": "90001"},
{"city": "Chicago", "state": "IL", "zip": "60601"},
{"city": "Houston", "state": "TX", "zip": "77001"},
{"city": "Miami", "state": "FL", "zip": "33101"},
]
def compare_markets(
restaurant_chain: str,
username: str,
password: str,
) -> dict[str, DeliveryZoneData]:
"""Compare delivery data across markets for a restaurant chain."""
results: dict[str, DeliveryZoneData] = {}
proxy = f"http://{username}-country-us:{password}@gate.hexproxies.com:8080"
for market in DELIVERY_MARKETS:
data = collect_zone_data(market["city"], proxy, f"https://www.doordash.com/food-delivery/{market['city'].lower()}-{market['state'].lower()}")
results = {**results, market["city"]: data}
time.sleep(random.uniform(10.0, 20.0))
return resultsBest Practices
- Use residential proxies — delivery platforms detect datacenter IPs
- Mobile User-Agents — food delivery is mobile-first
- US geo-targeting for domestic platforms
- 5-10 second delays between requests
- Location-specific requests — delivery data is hyper-local
Hex Proxies residential network with US targeting provides the local IP addresses that food delivery platforms expect for legitimate location-based content access.