Food delivery analysts
Coverage checks
Favorable to scraping
Compare which restaurants, categories, ratings, and price bands appear in a local delivery market, then export the result for territory or availability reporting.
Food Delivery$50Free
Uber Eats Restaurant Listings Scraper
This Uber Eats restaurant scraper turns public store URLs into a structured CSV of restaurant metadata and menu item rows. Use it when you need to scrape Uber Eats restaurants, export dishes and prices, and review market coverage without copying menu pages by hand.
Output
CSV
Columns
12
Input mode
Multi-URL
Source
Store pages
Template
Free import
At a glance
A local Uber Eats data extractor for restaurant and menu research
Exports restaurant and menu fields together
Each exported row keeps the restaurant context beside the dish name and price, so analysts can group menu items by store, cuisine category, address, rating, or source URL without joining multiple sheets.
Processes a prepared URL list
Add Uber Eats store URLs to the Navigate block and the workflow loops through them one by one. This is useful when your team already has target restaurants, city samples, or competitor pages to inspect.
Keeps exports under local control
The browser run and Structured Export happen inside UScraper. The template is not a hosted actor queue, and the CSV stays in the folder you choose unless you add another export step.
Handles dynamic menu pages pragmatically
The workflow waits, scrolls, and creates clean synthetic extraction rows after the page renders, which helps separate menu items from headers, ratings, add-to-cart labels, and repeated snippets.
Who this is for
Who needs Uber Eats restaurants and menu prices in CSV
Restaurant operators
Menu audits
Favorable to scraping
Review how your own listings and nearby competitors present menu items, prices, cuisine sections, and restaurant profile details across public store pages.
Agencies and researchers
Market mapping
Nuanced outcome
Build controlled samples for food delivery research, then enrich discovered websites with sibling workflows like the Website Contact Details Scraper.
How to use
From Uber Eats store URLs to a structured export
1
Import the template
Download the hosted JSON and import it into UScraper.
2
Add restaurant URLs
Replace the sample Navigate URL with the Uber Eats store pages for your target restaurants, city sample, or category research.
3
Run the page sequence
The workflow navigates, waits for page load, clicks common consent buttons when present, pauses briefly, and confirms that the page body is available.
4
Scroll and extract rows
A JavaScript block scrolls dynamic menu content, detects restaurant metadata, filters noisy snippets, and prepares rows for Structured Export.
5
Open the CSV
Structured Export appends rows with headers, then Loop Continue advances to the next store URL. Open the finished file in Excel, Sheets, or BI tools.
Output preview
What the Uber Eats restaurant scraper exports
ubereats-jp-restaurant-listings-scraper.csvColumn
restaurant_name
Store name from the visible heading, metadata, or page title fallback.
Column
restaurant_category
Cuisine or category cues collected from the rendered store page.
Column
address
Best-effort address text when the page exposes it.
Column
star_rating
Visible rating value when detected.
Column
rating_count
Review or rating count when available.
Column
dish_name
Detected menu item title after section headers and noisy labels are filtered.
Column
price_text
Displayed price string, including sale or multiple visible prices when present.
Column
restaurant_url
The final Uber Eats store URL from the browser location.
Sample rows
2 of many
| restaurant_name | restaurant_category | address | star_rating | rating_count | dish_name | price_text | restaurant_url |
|---|---|---|---|---|---|---|---|
| Matsuya Kita Nijuyon Jo | Japanese | Rice bowls | Hokkaido, Sapporo sample address | 4.6 | 500+ | Beef bowl set | ¥780 | |
| Matsuya Kita Nijuyon Jo | Japanese | Rice bowls | Hokkaido, Sapporo sample address | 4.6 | 500+ | Curry rice | ¥690 |
Headers included - one restaurant/menu item row per detected dish - open in Excel, Sheets, or BI tools
For broader discovery, pair this workflow with the Google SERP Scraper, the DuckDuckGo Search Results Scraper, or the existing Uber Eats Restaurant Listing Scraper when you only need restaurant names and URLs.
Frequently asked questions
Scraping Uber Eats can involve platform terms, robots guidance, copyright in menu content, privacy rules, and local law. Use this template only for permitted research, avoid bypassing access controls, keep request volume conservative, and get legal review before resale or large commercial datasets.
Before you scale
Practical limits for Uber Eats restaurant scraping
Limitations worth checking before larger runs
Prompts
Some sessions may ask for location, cookies, or verification
If a prompt blocks the page, resolve it manually and rerun a small sample before launching a longer batch.
Pacing
Keep row collection modest and observable
Heavy unattended runs can trigger throttling or incomplete pages. Start with 5-10 restaurants, inspect row quality, and only then expand the URL list.
Selectors
Dynamic menu layouts can change
The extraction block filters menu rows from rendered text and page metadata. Empty exports, missing prices, or repeated headers are signs that the workflow needs maintenance.
Browse more workflows in the UScraper template library or install the local desktop app from uscraper.io/download before importing this Uber Eats restaurant scraper.
Get Started
Download and use this template instantly
$50Free
What's Included
- Template JSON file ready to import
- Pre-configured scraping nodes
- Works with UScraper desktop app
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