How to Scrape DoorDash, Uber Eats, and Grubhub Menu Data in 2026

Food delivery platforms are among the harder scraping targets — they use aggressive anti-bot measures, require location parameters, and structure their data differently across platforms. Here's what actually works for extracting menu data, restaurant listings, and pricing.

DoorDash: Menu Data Extraction

DoorDash embeds menu data in the page's server-side rendered HTML as a JSON blob. This is the cleanest approach — no API authentication needed:

import requests, re, json from curl_cffi import requests as cf_requests

import requests, re, json from curl_cffi import requests as cf_requests

def scrape_doordash_menu(store_url: str) -> dict: """ Extract menu data from a DoorDash restaurant page.

URL format: https://www.doordash.com/store/restaurant-name-city-12345/ """ session = cf_requests.Session()

headers = { "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36", "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,/;q=0.8", "Accept-Language": "en-US,en;q=0.9", }

response = session.get(store_url, impersonate="chrome124", headers=headers)

if response.status_code != 200: return {}

html = response.text

DoorDash embeds data in a script tag with id="NEXT_DATA" (Next.js)

This contains the full menu structure

pattern = r'(.?)' match = re.search(pattern, html, re.DOTALL)

if not match: return {}

try: next_data = json.loads(match.group(1)) except json.JSONDecodeError: return {}

Navigate to menu data

Path: props.pageProps.storeMenu or props.pageProps.initialState

page_props = next_data.get('props', {}).get('pageProps', {})

Try different paths where DoorDash stores menu data

menu = ( page_props.get('storeMenu') or page_props.get('initialState', {}).get('storeMenu') or {} )

return parse_doordash_menu(menu)

def parse_doordash_menu(raw_menu: dict) -> dict: """Extract structured menu items from DoorDash's data format""" result = { 'store_name': '', 'categories': [] }

DoorDash menu structure: menuCategories -> items

categories = raw_menu.get('menuCategories') or raw_menu.get('categories', [])

for category in categories: cat_items = [] for item in category.get('items', []): cat_items.append({ 'name': item.get('name', ''), 'price': item.get('price', 0) / 100, # In cents 'description': item.get('description', ''), 'calories': item.get('calories', ''), 'id': item.get('id', ''), })

if cat_items: result['categories'].append({ 'name': category.get('name', ''), 'items': cat_items })

return result

Usage

menu = scrape_doordash_menu("https://www.doordash.com/store/the-heights-deli-los-angeles-2501711/") for category in menu.get('categories', [])[:3]: print(f"\n{category['name']}:") for item in category['items'][:5]: print(f" {item['name']}: ${item['price']:.2f}")`

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DoorDash: Location-Based Restaurant Search

def search_doordash_restaurants(lat: float, lng: float, keyword: str = "") -> list:  """  Search for restaurants in a location area.  Uses DoorDash's unofficial API.  """  session = cf_requests.Session()

def search_doordash_restaurants(lat: float, lng: float, keyword: str = "") -> list:  """  Search for restaurants in a location area.  Uses DoorDash's unofficial API.  """  session = cf_requests.Session()

DoorDash uses a GraphQL-like API for search

api_url = "https://www.doordash.com/graphql/getRestaurantFeed"

payload = { "operationName": "getRestaurantFeed", "variables": { "lat": str(lat), "lng": str(lng), "offset": 0, "limit": 20, "filters": {}, } }

headers = { "Content-Type": "application/json", "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36", "Referer": "https://www.doordash.com/", "Origin": "https://www.doordash.com", }

r = session.post(api_url, impersonate="chrome124", json=payload, headers=headers)

if r.status_code != 200: return []

data = r.json() restaurants = data.get('data', {}).get('restaurantFeed', {}).get('storeHeaders', [])

return [{ 'name': r.get('name', ''), 'id': r.get('id', ''), 'rating': r.get('averageRating', ''), 'delivery_time': r.get('status', {}).get('pickupTime', ''), 'url': f"https://www.doordash.com/store/{r.get('name','').lower().replace(' ','-')}-{r.get('id','')}/" } for r in restaurants]`

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Uber Eats: Menu via Embedded JSON

Uber Eats similarly embeds data in its Next.js page structure:

def scrape_ubereats_menu(restaurant_url: str) -> dict:  """  Scrape Uber Eats restaurant menu from embedded JSON.  URL: https://www.ubereats.com/store/restaurant-name/UUID  """  session = cf_requests.Session()

def scrape_ubereats_menu(restaurant_url: str) -> dict:  """  Scrape Uber Eats restaurant menu from embedded JSON.  URL: https://www.ubereats.com/store/restaurant-name/UUID  """  session = cf_requests.Session()

headers = { "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36", "Accept-Language": "en-US,en;q=0.9", }

r = session.get(restaurant_url, impersonate="chrome124", headers=headers) html = r.text

Uber Eats embeds data in: window.REDUX_STATE or in a JSON script tag

Method 1: Redux state

redux_match = re.search(r'window.REDUX_STATE\s*=\s*({.?});', html, re.DOTALL) if redux_match: try: state = json.loads(redux_match.group(1))

Navigate to menu in Redux state

menu_data = state.get('routeData', {}).get('storeInfo', {}) return extract_ubereats_menu(menu_data) except json.JSONDecodeError: pass

Method 2: Script tags with data-state or similar

for script in re.findall(r']>(.?)', html, re.DOTALL): if '"catalogSectionsMap"' in script or '"menuSections"' in script: try: data = json.loads(script) return extract_ubereats_menu(data) except: continue

return {}

def extract_ubereats_menu(data: dict) -> dict: sections = data.get('catalogSectionsMap') or data.get('menuSections', {}) result = {'categories': []}

for section_id, section in sections.items(): items = [] for item in section.get('catalogItems', section.get('items', [])): items.append({ 'name': item.get('title', ''), 'price': item.get('priceTagline', '') or str(item.get('price', 0)), 'description': item.get('description', ''), }) if items: result['categories'].append({ 'name': section.get('title', ''), 'items': items })

return result`

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Grubhub: Simpler HTML Extraction

Grubhub's pages are less JavaScript-heavy and easier to parse:

from bs4 import BeautifulSoup

def scrape_grubhub_menu(restaurant_url: str) -> list: """ Extract menu from Grubhub restaurant page. """ session = cf_requests.Session()

r = session.get(restaurant_url, impersonate="chrome124", headers={"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36"})

Try JSON-LD schema first (structured data)

soup = BeautifulSoup(r.text, 'html.parser')

items = []

Grubhub often includes schema.org/MenuItem

for script in soup.find_all('script', type='application/ld+json'): try: data = json.loads(script.string) if data.get('@type') == 'Restaurant' and 'hasMenu' in data: menu = data['hasMenu'] for section in menu.get('hasMenuSection', []): for item in section.get('hasMenuItem', []): items.append({ 'name': item.get('name', ''), 'price': item.get('offers', {}).get('price', ''), 'description': item.get('description', ''), 'category': section.get('name', ''), }) except: continue

if items: return items

Fallback: HTML parsing

for item_div in soup.find_all('div', class_=lambda c: c and 'menuItem' in c): name = item_div.find(class_=lambda c: c and 'name' in c.lower()) price = item_div.find(class_=lambda c: c and 'price' in c.lower())_

if name and price: items.append({ 'name': name.text.strip(), 'price': price.text.strip(), })

return items`

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Handling Anti-Bot on Food Delivery Apps

These platforms use progressively stronger detection:

Platform Detection Level Best Approach

Grubhub Basic curl_cffi + Chrome impersonation

Uber Eats Moderate curl_cffi + residential proxy

DoorDash Moderate-High curl_cffi + proxy + session warm-up

Deliveroo (UK) High Playwright + camoufox

import time, random

def robust_food_delivery_scrape(scrape_func, url: str, max_retries: int = 3): """Wrapper that handles rate limiting for food delivery sites"""

for attempt in range(max_retries): try: result = scrape_func(url)

if result: return result

Empty result might mean soft block

print(f"Empty result on attempt {attempt+1}, waiting...") time.sleep(10 * (attempt + 1)) # Progressive backoff*

except Exception as e: print(f"Attempt {attempt+1} failed: {e}") time.sleep(5 * (attempt + 1))*

return {}`

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Batch Scraping Strategy

For scraping multiple restaurants:

import time, random

def scrape_restaurant_list(store_urls: list, delay_range=(3, 7)) -> list: """ Scrape multiple restaurants with human-like pacing. """ results = []

for i, url in enumerate(store_urls): print(f"[{i+1}/{len(store_urls)}] Scraping: {url[:60]}")

Rotate function based on platform

if 'doordash' in url: data = scrape_doordash_menu(url) elif 'ubereats' in url: data = scrape_ubereats_menu(url) elif 'grubhub' in url: data = {'items': scrape_grubhub_menu(url)} else: continue

if data: results.append({'url': url, 'data': data})

Human-like delay between requests

delay = random.uniform(delay_range) time.sleep(delay)

Longer break every 10 restaurants

if (i + 1) % 10 == 0: print("Taking a longer break...") time.sleep(random.uniform(30, 60))

return results`

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Saving to CSV/JSON

import csv, json

def save_menu_to_csv(menu_data: dict, filename: str): """Flatten menu items to CSV""" rows = [] store_name = menu_data.get('store_name', '')

for category in menu_data.get('categories', []): for item in category.get('items', []): rows.append({ 'store': store_name, 'category': category.get('name', ''), 'item_name': item.get('name', ''), 'price': item.get('price', ''), 'description': item.get('description', '')[:100], 'calories': item.get('calories', ''), })

if rows: with open(filename, 'w', newline='') as f: writer = csv.DictWriter(f, fieldnames=rows[0].keys()) writer.writeheader() writer.writerows(rows) print(f"Saved {len(rows)} items to {filename}")`

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Related Articles

• Web Scraping Without Getting Banned in 2026 — Full anti-detection playbook

• curl_cffi Stopped Working? Here's What to Try Next — When basic curl_cffi isn't enough

• Amazon's Anti-Bot Strategy — ML-based detection patterns

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