We extract restaurant catalogues, dynamic delivery fees, menu items, and pandamart inventory from Foodpanda. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Restaurant Listings objects from foodpanda.com. All fields typed and schema-versioned.
"restaurant_id": "r1a2b3", "name": "Burger King - Marina Bay", "rating": 4.6, "review_count": 1240, "delivery_fee": 3.49, "is_pandapro": true
| # | restaurant_id | name | chain_name | cuisine_tags | rating | review_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Menu Items objects from foodpanda.com. All fields typed and schema-versioned.
"item_id": "i8x9y0", "item_name": "Whopper Meal", "base_price": 12.5, "currency": "SGD", "is_sold_out": false, "popular_flag": true
| # | item_id | restaurant_id | category_name | item_name | description | base_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Modifiers & Options objects from foodpanda.com. All fields typed and schema-versioned.
"group_name": "Choose your drink", "selection_type": "SINGLE", "option_name": "Coke Zero", "additional_price": 0.0, "is_available": true, "max_selections": 1
| # | modifier_group_id | item_id | group_name | selection_type | min_selections | max_selections |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for pandamart (Groceries) objects from foodpanda.com. All fields typed and schema-versioned.
"product_id": "pm4567", "product_name": "Farm Fresh Milk 1L", "brand": "Farm Fresh", "price": 4.2, "discount_price": 3.8, "stock_status": "IN_STOCK"
| # | store_id | product_id | ean_code | brand | product_name | category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Promotions & Fees objects from foodpanda.com. All fields typed and schema-versioned.
"restaurant_id": "r1a2b3", "delivery_fee": 5.99, "surge_active": true, "voucher_code": "PANDAPRO50", "discount_percentage": 50, "max_discount_value": 10.0
| # | restaurant_id | location_id | timestamp | delivery_fee | service_fee | surge_active |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Foodpanda scraper handles complex location spoofing, API interception, and nested menu normalisation - ensuring you get accurate delivery fees and inventory without triggering Cloudflare blocks.
Inject exact latitude and longitude coordinates to capture accurate, hyper-local menus, delivery fees, and restaurant availability.
Extract deep nested JSON structures for customisable items, capturing add-ons, minimum selections, and conditional pricing.
Monitor fluctuating delivery and service fees based on time of day, weather conditions, and driver availability.
Track grocery SKUs, stock levels, brand details, and promotional pricing across dark store locations.
Capture campaign-specific discounts, minimum basket sizes, and pandapro subscription benefits.
Unified schema covering operations in Singapore, Malaysia, Thailand, Pakistan, Philippines, and other APAC markets.
Extract standard opening times, public holiday schedules, and temporary closure statuses for every outlet.
Group individual franchise locations under parent chain identifiers for accurate brand-level analytics.
Execute daily catalog refreshes or run high-frequency hourly pipelines to monitor surge pricing during peak meal times.
Brief in. Clean data out.
Provide target cities, coordinate grids, or specific restaurant URLs. We design the extraction schema together.
We configure coordinate injection, API interception, proxy rotation, and anti-bot circumvention for foodpanda.com.
Schema validation, location accuracy checks, fee outlier detection, and menu structure tests before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Foodpanda relies on heavy client-side rendering and strict location-based API rate limits. Here is how we maintain extraction stability.
Foodpanda does not expose global catalogues. Every request must originate from a specific latitude and longitude. We map target cities into 1km hexagonal grids and inject coordinates at the browser level to ensure complete coverage of delivery zones.
Parsing the DOM for menus is slow and fragile. Our Playwright instances intercept the underlying GraphQL and REST API responses during page hydration, extracting the raw JSON payloads before they hit the rendering engine.
Foodpanda employs aggressive bot protection. We rotate city-specific residential proxies, spoof TLS fingerprints, and maintain valid cookie sessions to blend in with legitimate mobile and web traffic.
A single burger can have thousands of permutations. We flatten these nested modifier groups (e.g., size, extra cheese, drink choice) into relational tables or deeply nested JSON arrays without losing the pricing logic.
Foodpanda operates across multiple countries with different currencies, tax structures, and fee naming conventions. Our pipeline normalises these variations into a single, predictable schema for your warehouse.
Ghost kitchen operators analyse local menu gaps and competitor pricing to launch highly targeted virtual brands.
QSR chains track delivery markups and promotional frequency across aggregators to maintain price parity.
Beverage and snack brands monitor pandamart to track out-of-stock rates, share of shelf, and category positioning.
Delivery platforms compare their own surge pricing and restaurant availability against Foodpanda in real time.
Marketing teams analyse voucher mechanics, minimum spend requirements, and discount caps to optimise campaign spend.
Real estate teams use delivery time estimates and restaurant density maps to identify underserved geographic zones.
"Foodpanda holds the most accurate hyper-local commerce graph in APAC, but accessing it requires continuous location spoofing and API reverse-engineering."
Most teams fail at food delivery scraping because they rely on static IPs and basic HTTP clients. Extracting accurate delivery fees and pandamart inventory requires precise coordinate injection, TLS fingerprinting, and session persistence. DataFlirt manages the proxy rotation and API payload extraction so your team receives clean, structured JSON.
Everything supported by our foodpanda.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
We bypass fragile DOM parsing by intercepting Next.js and React hydration states, extracting the raw JSON data directly from the network layer.
We route requests through city-specific residential IPs that match the injected GPS coordinates, preventing location-mismatch bans.
Pipelines run on AWS Lambda and ECS, allowing us to scale concurrency instantly to capture surge pricing during peak lunch and dinner hours.
Data delivered to where your team already works — no new tooling required.
About foodpanda.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Foodpanda is generally permissible under applicable law. DataFlirt targets only public, non-authenticated restaurant, menu, and pricing data. We do not extract personal user data or order histories.
We use browser-level coordinate injection (latitude/longitude) combined with city-matched residential proxies. This ensures the Foodpanda backend serves the exact menu and delivery fees applicable to that specific micro-location.
Yes. We extract full pandamart catalogues, including grocery SKUs, EAN codes, stock availability, and promotional pricing across all active dark store locations.
For dynamic pricing analysis, we can configure pipelines to run hourly during peak meal times (e.g., 11:00 AM to 2:00 PM) to capture surge pricing and delivery delays.
Yes. We extract the full modifier graph, including minimum/maximum selection rules, add-on pricing, and nested choices, delivering them as structured JSON arrays or relational tables.
We support all major APAC markets including Singapore, Malaysia, Thailand, Pakistan, Philippines, Bangladesh, Hong Kong, and Taiwan, using a normalised schema.
We utilise full Playwright browser sessions with TLS fingerprint spoofing, realistic request headers, and residential proxy rotation to bypass automated bot challenges without manual intervention.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily pandamart inventory sync or hourly delivery fee tracking across 10,000 restaurants - we scope, build, and operate the pipeline. Tell us what you need.
