DoorDash shows you the restaurants that will deliver to one address, each with a rating, a review count, an estimated delivery window, a fee and a distance. This service turns that listing into a table - one row per restaurant, nine columns - so you can compare a whole market instead of scrolling one card at a time.
On DoorDash there is no such thing as a result that is true everywhere. Change where you are standing and every column changes with you.
DoorDash is a delivery marketplace, so the listing it builds is an answer to a question about a place: which restaurants will deliver to this address, right now. Move the address a few miles and restaurants drop off the list, others appear, and the ones that survive come back with a different distance, a different delivery_time and often a different delivery_fee. None of those three are properties of the restaurant. They are properties of the restaurant as seen from somewhere.
The same is true of time. Delivery estimates widen when kitchens are busy, and fees move with demand and distance. An export captured at seven on a Friday evening and one captured at three on a Tuesday afternoon are both correct and are not comparable. If you intend to track anything over time, record when you ran it and hold the address constant.
One more thing that catches people out: delivery_fee is the figure shown to a customer without a subscription. DashPass members see no delivery fee at all on eligible orders that meet the subtotal minimum. So the column tells you what DoorDash quotes an ordinary visitor for that restaurant from that address - a useful and real number, but not the amount every customer pays.
Set the address on DoorDash, get the listing you want on screen, and hand it over.
Narrow the listing on DoorDash before you export it. Filtering there is free; exporting a broad listing costs rows.
These are the columns this scraper's export declares, in order. Each description explains what the column is for and what DoorDash shows in that position on a store card.
rating, not as the denominator of it: the same score from a handful of ratings and from thousands of them are very different claims.Three of these columns are about a journey, not a restaurant. delivery_time, delivery_fee and distance all describe the relationship between a restaurant and the one address you set, so they are only comparable within a single export from a single address. name, rating, reviews and url travel with the restaurant and can be compared across exports - and url is the stable thing to match a restaurant on, because names repeat across a city.
None of these are quirks of the scraper. They are properties of how a delivery marketplace works, and they decide what the export can honestly be used for.
A DoorDash listing is generated for one delivery address, which means your export has an implicit column that is not in the file. Put it back: store the address alongside the run, or run one address per file. Merging two addresses into one table without that marker produces a dataset where distance and delivery_fee silently mean different things on different rows, and nothing in the data will tell you.
DoorDash's store rating is a Lifetime Rating on a 1–5 scale, and DoorDash states that the exact calculation is not disclosed and weights recent feedback. So rating and reviews will not reconcile arithmetically, and they are not supposed to. Use reviews as a weight and a sample size, and do not attempt to recover a distribution from a mean that was never a mean.
delivery_time is a quoted window, not a measured duration, and it widens exactly when you would expect - a busy Friday evening, bad weather, a kitchen already deep in tickets. That makes it a genuinely interesting thing to sample repeatedly, and a poor thing to sample once. A single export tells you what was being quoted at that moment; a schedule tells you how a neighbourhood behaves.
DashPass members pay no delivery fee on eligible orders that meet the subtotal minimum, and those minimums themselves vary by store, city and time of day. delivery_fee is therefore the quote an ordinary visitor gets, which is the right number for comparing what restaurants charge to reach an address - and the wrong number for estimating what a typical customer actually pays. Say which one you mean before you publish it.
Where a delivery listing is more useful as a spreadsheet than as an app screen.
Before signing a lease or launching a delivery-only kitchen, export the listing for that address. You get every competitor that reaches it, with a rating and a review count against each - which is the difference between "there are a few burger places nearby" and knowing exactly how many, how well reviewed, and how long they take. Run the same export at three candidate addresses and the comparison writes itself.
delivery_fee and delivery_time are the two things a hungry person actually compares, and they are quoted per restaurant per address. Pull the listing your own restaurant appears in and you can see, in one column, whether you are the cheapest or the slowest option on the screen a customer is looking at - a much sharper question than what your own dashboard shows you.
One export is a photograph. Run the same address on a schedule and match rows on url, and you get arrivals and departures - new restaurants appearing on the platform, others going quiet - plus how quoted times and fees drift by hour and by day. That is the sort of data nobody publishes and everybody wants.
No subscription, no minimum, no per-seat licence. Your first 500 rows are on us - after that it is pay-as-you-go.
For every new account, one time. No credit card. All scrapers unlocked. A single neighbourhood listing is usually a few hundred restaurants at most, so a real first export often costs nothing at all.
Roughly $2 per 1,000 restaurants. Because billing follows rows, the cheapest way to work is to narrow the listing on DoorDash first - a cuisine or a category rather than everything that delivers - and to survey the addresses you care about rather than a grid of them.
Volume pricing, SLAs, dedicated workers and tailored onboarding for teams tracking delivery markets across many addresses and cities on a repeating schedule. Tell us your numbers and we will quote.
LIVESCRAPER10 at checkout.Plenty of restaurants are on Google and not on DoorDash, and the ones on both are described differently in each place. These two fill the gaps.
Every restaurant in an area, delivery or not - with address, phone, website and opening hours, which a DoorDash card does not carry.
The reviews behind a rating rather than the count of them - useful when a DoorDash score and a Google score disagree and you want to know why.
Short answer: yes. A delivery listing is a shop window, published so that as many people as possible see it.
DoorDash's restaurant listings are public. Anyone can browse them without an account, and collecting publicly visible business information for research is long-settled practice. As long as the data is publicly available and the process does not disrupt the site, there are no federal laws against it.
The subject matter is about as uncontroversial as scraping gets. Every one of these nine columns describes a business - its name, its score, what it charges to deliver and how far away it is. There is no personal data here: no customer, no driver, no reviewer, not even a review body. Nothing in the export identifies a person.
DoorDash's terms restrict automated access, so this remains a question of terms. We touch nothing behind a login, place no orders, run no third-party trackers on the data layer, and your exports self-delete after 30 days.
The questions we hear most. Something else? Talk to us - humans write the answers, not bots.
query, name, rating, reviews, delivery_time, delivery_fee, distance, url and status. One row per restaurant. status is a per-row flag from our export layer rather than something DoorDash publishes.delivery_time, delivery_fee and distance are all measured from the delivery address, so they only mean the same thing within one address. rating, reviews and name travel with the restaurant and compare fine. Keep query so you can always tell the rows apart.reviews as a sample size that tells you how much confidence the score deserves, not as the denominator of an average you can reconstruct.url. Restaurant names repeat across a city - chains especially - and the same brand can appear several times in one listing at different addresses. The store URL is the one value in the export that identifies a specific store rather than a name.Set the address, paste the listing, and take the restaurants away as rows - name, rating, review count, delivery window, fee and distance. Your first 500 rows are free.
DoorDash is one of the largest food delivery marketplaces in the United States, and for a great many restaurants it is now the shop window that matters most. Its listing page answers a specific question - which restaurants will deliver to this address - and answers it with a wall of cards, each carrying a name, a star rating, a review count, an estimated delivery window, a delivery fee and a distance. That design is right for a hungry person choosing dinner. It is wrong for anyone asking a question about the market rather than about one meal: how many competitors reach this postcode, how they are rated against each other, what the going delivery charge is on this street, which restaurants appeared this month and which quietly stopped. The DoorDash Scraper answers those by turning the listing into a table: one row per restaurant, nine columns, exported as CSV, JSON or Excel.
The single most important thing to understand about this data is that a DoorDash listing is generated for one delivery address. That is not a detail - it is the whole frame. Distance is measured from that address. The delivery window is quoted for that journey. The fee varies by merchant and by how far the restaurant is from where you asked it to deliver. Even the membership of the list is address-dependent: move a few miles and restaurants drop out while others appear. The export therefore has an implicit column that is not in the file, and the discipline that makes this data trustworthy is putting it back - one address per file, or the address stored alongside the run. Merge two addresses without that marker and you have a table in which the same column means different things on different rows, with nothing in the data to warn you.
Two further properties are worth planning for. The store rating is a Lifetime Rating on a 1–5 scale, and DoorDash states plainly that the exact calculation is not disclosed and that it weights recent feedback; it is not the arithmetic mean of the reviews counted beside it, and it will not reconcile with them. Use the review count as a confidence weight and a sample size rather than as the denominator of an average you can rebuild. And the delivery fee is the figure quoted to a visitor without a subscription: DashPass members pay no delivery fee on eligible orders meeting a subtotal minimum, and those minimums themselves vary by store, city and time of day. Both numbers are real and useful. Both describe a specific viewer in a specific place at a specific moment, and saying which is the difference between an analysis that holds up and one that quietly does not.
One honest note about the evidence behind this page. Every claim above is either the export's declared schema - the nine column names, identical on both of our local runs - or a documented property of DoorDash itself, taken from DoorDash's own help centre and merchant material. There are no scrape results here, because we have none: both of our runs were made against a placeholder URL rather than a real DoorDash listing and returned nothing, and the service is recorded internally as needing a residential proxy. So this page quotes no restaurant counts, no ratings, no fees and no delivery times, and none will be added unless a real export backs them. What the page does describe - what each column is for, and what DoorDash puts in that position - is accurate. Start free: your first 500 rows cost nothing and need no credit card.