Platform · foodpanda

foodpanda Data Scraping

A footprint that has changed repeatedly. Any dataset built on an assumed market list will be wrong within a year.

foodpanda data scraping collects restaurant menus, pandamart own-store grocery, pricing and availability across foodpanda's Asian markets. The characteristic that shapes the engagement is footprint volatility: markets have been exited and one is subject to a pending sale, so we record which markets were actually observed in each batch rather than working from a list.

Most platform pages can assume a market list. This one cannot, and pretending otherwise produces a dataset that silently stops covering what it claims to.

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foodpanda.jsonl LIVE FEED

{"market":"SG","surface":"pandamart", "price_setter":"foodpanda", "item_name_local":"Example noodles 400g", "item_price":4.20,"currency":"SGD", "pack_size":400,"pack_unit":"g", "markets_observed":["SG","MY","PH","HK","TW","PK","BD","KH","LA","MM"]} {"market":"TW", "market_status_note":"agreed sale to another operator, under regulatory review", "note":"if it completes, the platform behind this market is a different company"} {"market":"TH", "markets_observed":"absent", "market_status_note":"exited May 2025", "caution":"a series spanning this shows a STRUCTURAL change, not a collection failure"}

3 of 4,204,110 merchant-item rows markets_observed recorded per batch · never assumed · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to foodpanda or its owners. foodpanda and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

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foodpanda at a glance

How we handle foodpanda specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Platform: foodpanda — Delivery Hero's Asian brand
Own-store format: pandamart, which is a separate surface from partner restaurants
The characteristic: Footprint has changed repeatedly. Thailand exited in 2025
Pending change: Taiwan is subject to a sale to Grab, under regulatory review
What we do about it: markets_observed recorded per batch, never assumed
Price setter: Merchant for restaurants; foodpanda for pandamart
Languages: Many scripts. Retained as published, never translated into the record
Refresh: Daily standard; sub-daily where promotional intensity is the question

Platform specifics

What is specific to foodpanda

These are the reasons a foodpanda dataset needs its own handling rather than a shared retail schema.

A moving footprint, and why we record rather than assume

foodpanda's market list has changed more than most platforms'. Thailand was exited in May 2025 after thirteen years. Germany and Japan were exited earlier. Denmark, Ghana, Slovakia and Slovenia were discontinued before that.

And there is a change pending: Taiwan is subject to an agreed sale to Grab, under regulatory review. If it completes, the Taiwanese platform is no longer foodpanda — which for a dataset is not a detail.

What that means for a feed

A dataset built on an assumed market list will silently stop covering a market it claims to.
A historical series spanning an exit contains a break that looks like a collapse in coverage.
A market changing hands means the platform behind the same URL is a different company.

We record markets_observed in every batch — the markets where collection actually returned data — and flag market_status_note where a change is known or pending. That way a coverage gap is visible as a structural change rather than a collection failure.

We are describing publicly reported corporate events, not predicting outcomes. Where a transaction is under review we say it is under review.

pandamart is a separate surface from partner restaurants

foodpanda carries partner restaurants and pandamart, its own dark-store grocery format. Different inventory, different price setter, different mechanics.

pandamart is foodpanda-owned with foodpanda-set prices and retail grocery packs.
Partner restaurants set their own prices, on prepared items with modifier groups.
Availability behaves differently: pandamart stockouts are inventory; a restaurant being unavailable is usually a merchant pausing.

Every record carries surface and price_setter. This is the same structure as DashMart inside DoorDash and Wolt Market inside Wolt, and it is the field that stops a price movement being unattributable.

It also means the "Delivery Hero own-store" question is answered here rather than needing its own page — pandamart is the format, foodpanda is the app it sits in.

Wide language and market-maturity variation

The footprint spans markets at very different stages, from mature city-states to markets where delivery is comparatively new, and across scripts including Latin, Chinese, Bengali, Urdu, Khmer, Lao and Burmese.

Item names are retained exactly as published, in their original script, with the dominant script recorded.
No machine translation into the name field. Across this script range it would make originals and translations indistinguishable within weeks.
Matching runs on structure, price and position rather than title similarity, which performs badly here.

Modifiers, as everywhere

Restaurant items carry modifier groups where required selections make the headline price not the entry price. We deliver them structured with is_required and option prices, and compute min_realisable_price with the basis recorded — the same discipline our Keeta and iFood pages apply.

Scope

What we collect on foodpanda, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

Restaurant and pandamart records with surface and price_setter on each
markets_observed recorded per batch, never assumed from a list
market_status_note where a change is known or pending
Modifier groups with required flags and option prices
Minimum realisable price computed on a stated basis
Pack parsed for pandamart, with unit price on a stated basis
Delivery, service and small-order fees as separate fields
Item names in original script, with dominant script recorded
Serviceability as a distinct state from a store being closed

❌ What we do not, and why

A market list assumed rather than observed
A blended price across pandamart and partner restaurants
Machine translation written into item name fields
Predictions about pending transactions
Sales, order volumes, customer, rider or order data

Core foodpanda fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field	What it is on this platform
market / markets_observed	Which market, and what the batch actually covered
market_status_note	Where a change is known or under review
surface / price_setter	restaurant or pandamart, and who sets the price
merchant_id / merchant_name	Partner merchant or pandamart store
item_id / item_name_local / script_dominant	Original text and its script
item_price / currency	Headline price
modifier_groups / min_realisable_price / price_basis	The number a customer can actually pay
pack_size / pack_unit / price_per_unit	pandamart grocery lines
delivery_fee / service_fee / small_order_fee	Each separately
store_open / serviceable	Two distinct states
observed_at	Timestamp

Use cases

What teams do with foodpanda data

Multi-market Asian competitive tracking

Markets recorded per batch rather than assumed, so a series spanning a market exit shows a structural change instead of what looks like a collection failure.

pandamart versus partner restaurant positioning

Surface and price setter on every record, so foodpanda's own-store pricing against the merchants on its platform is a deliberate comparison.

Menu pricing that reflects what customers pay

Minimum realisable price rather than headline item price, which is the difference between two merchants ranking as equivalent and ranking meaningfully apart.

Market-structure change monitoring

markets_observed and status notes over time, which is the cheapest way to see a platform's footprint move before it affects your coverage.

The 24-hour sample — run on your sources, not ours

Send us a foodpanda item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

Real extraction from your actual sources
Returned within 24 hours
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

foodpanda is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. foodpanda data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what food & restaurant data covers, and a foodpanda-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

foodpanda data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

Roughly ten Asian markets, and the honest answer is that we record what each batch actually observed rather than working from a list. Thailand was exited in May 2025, and Germany and Japan earlier.

There is also a pending change: Taiwan is subject to an agreed sale to Grab under regulatory review. If it completes, the platform behind that market is a different company.

Because a feed built on an assumed market list silently stops covering a market it claims to, and a historical series spanning an exit contains a break that looks like a coverage collapse.

markets_observed per batch makes the difference visible: a structural change rather than a collection failure.

No. pandamart is foodpanda's own dark-store grocery format with foodpanda-set prices and retail packs. Partner restaurants set their own prices on prepared items with modifier groups.

Records carry surface and price_setter so a price movement is attributable to the right party.

No, and deliberately. The own-store format runs as pandamart inside foodpanda, talabat mart inside talabat and Glovo stores inside Glovo — and all three of those apps already have pages here.

A separate page would duplicate three we have rather than adding anything.

Item names are retained exactly as published with the dominant script recorded. Matching runs on structure, price and position rather than title similarity, which performs badly across Latin, Chinese, Bengali, Urdu, Khmer, Lao and Burmese in one dataset.

We do not machine-translate into the name field.

We quote individually. Market count is the dominant driver rather than merchant count, because each market's language, currency and category structure needs its own handling.

One scoping call, a free pilot within 24 hours, then a fixed monthly quote. Request a quote.

See real foodpanda data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

Get a free sample Talk to an engineer

Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

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