Restaurant market researchers
Area studies
Favorable to scraping
Build a reviewable dataset for selected Tokyo, Osaka, Kyoto, or niche cuisine pages before deeper manual checks.
Restaurant Directory$50Free
Tabelog Restaurant Scraper for Store Details
This Tabelog restaurant scraper turns selected Tabelog store detail URLs into a structured CSV for restaurant research, market mapping, and lead qualification. Import the workflow into the UScraper local desktop app, add the Tabelog restaurant pages you are allowed to collect, and export names, ratings, review counts, categories, reservation phone, address, transportation, hours, budget, and payment fields without writing automation code.
Output
CSV
Fields
11
Inputs
Detail URLs
Flow
Multi-URL
Template
Free
At a glance
Export Tabelog restaurant details to CSV
This template is focused on detail pages. The Navigate block includes sample Tokyo restaurant URLs, then the loop repeats the same load, wait, export, and pause sequence for every URL in the list. That makes it useful when you already have Tabelog restaurant pages from manual research, an approved list, or a separate discovery workflow.
Use the export for spreadsheet-first work: compare restaurant categories, review volume, budget bands, transport notes, and operating-hour text across a curated set of stores. For a discovery-first workflow, pair it with the Tabelog Store List Scraper, then run this detail scraper on the restaurant URLs you choose to inspect more deeply.
CSV rows with restaurant context
Export store identity, Tabelog URL, rating, reviewer count, contact, address, access notes, hours, budget, and payment method into one local file.
Multi-URL batch input
Add more restaurant detail URLs to navigate.urls and the workflow will visit each page, wait, export, pause, and continue.
Local desktop execution
The stock workflow writes the CSV to your configured save folder and does not route rows through a hosted scraping actor.
Built for detail accuracy
No detail-page pagination is needed for the captured fields, so the automation stays compact and easy to audit.
Who this helps
Tabelog data extractor use cases
Hospitality operators
Competitor tracking
Favorable to scraping
Export rating, category, hours, budget, and payment information for nearby competitors and compare changes over time.
Agencies and analysts
Client research
Nuanced outcome
Combine Tabelog exports with the Guru Navi restaurant scraper, Hot Pepper restaurant listing scraper, and TripAdvisor restaurant detail scraper for broader source coverage.
How to use
Configure the Tabelog restaurant scraper workflow
2
Add restaurant detail URLs
Replace the sample Tabelog URLs in the Navigate block with the restaurant pages you want to collect. Keep one detail page per URL.
3
Confirm the export path
Structured Export writes Tabelog-Store-list-detail-Scraper.csv with headers enabled and append mode on. Change the save folder before client or production runs.
4
Run the loop
UScraper navigates to a URL, waits for page load, waits until a visible h2 appears, exports the body-level fields, sleeps for one second, and continues.
5
Open and audit the CSV
Check a few rows against the source pages before using the file in dashboards, enrichment tools, or outreach lists.
Output preview
CSV columns produced by the template
The export uses one row per Tabelog detail page. Japanese table labels are read from the rendered page, normalized, and appended to the same CSV so the file can open in Excel, Google Sheets, BI tools, or a restaurant research database.
| Restaurant_name | Page_URL | Star_rating | Number_Of_Reviewers | Categories | Address | Operating_hours | Budget |
|---|---|---|---|---|---|---|---|
| Ginza Sushi Sample | https://tabelog.com/tokyo/A1301/A130101/12345678/ | 3.62 | Reviews 248 people | Sushi, Japanese | Tokyo, Chuo-ku Ginza 1-2-3 | 11:30-14:00 / 17:30-22:00 | Dinner: JPY 10,000-14,999 |
| Kagurazaka Bistro Example | https://tabelog.com/tokyo/A1309/A130905/13024910/ | 3.48 | Reviews 91 people | Bistro, Wine bar | Tokyo, Shinjuku-ku Kagurazaka 4-5 | 18:00-23:00 | Dinner: JPY 6,000-7,999 |
Tabelog-Store-list-detail-Scraper.csvColumn
Restaurant_name
Visible restaurant name from the main page heading.
Column
Page_URL
Current Tabelog detail page URL.
Column
Star_rating
Rating parsed from page metadata or visible rating elements.
Column
Number_Of_Reviewers
Visible review-count text where present.
Column
Categories
Genre/category text from the restaurant information table.
Column
Tel_for_reservation
Reservation contact field when displayed.
Column
Address
Address text cleaned of map helper copy.
Column
Transportation
Access or nearest transport instructions.
Column
Operating_hours
Opening-hour text from the detail table.
Column
Budget
Budget or review-aggregate budget field.
Column
Method_of_payment
Payment method text where Tabelog exposes it.
Headers included - sample rows are illustrative because no CSV sample shipped with the bundle
Frequently asked questions
Tabelog pages may be publicly viewable and still governed by Tabelog terms, robots rules, copyright, database rights, privacy law, and local regulations. Keep runs modest, do not bypass access controls, and get legal review before commercial redistribution, resale, or model training.
Before you run
Practical limits and maintenance notes
Guardrails for reliable Tabelog exports
Scope
This is a detail-page scraper
The supplied workflow does not crawl search-result pagination. Prepare restaurant detail URLs first, then let the multi-URL loop export each page.
Layout
Selectors may need updates after Tabelog changes
Empty rating, budget, or payment cells usually mean the page layout, locale, or restaurant information table changed. Re-test one URL before a large batch.
Policy
Review Tabelog rules before reuse
Check Tabelog terms of use and robots.txt, keep request volume conservative, and avoid uses that require licensed access.
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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