MealMe offers in-depth restaurant menu data, including prices, from the top 100,000 restaurants across the USA and Canada. Our proprietary technology collects accurate, real-time menu and pricing information, enabling businesses to make data-driven decisions in competitive intelligence, pricing optimization, and market research. With comprehensive coverage that spans major restaurant platforms and chains, MealMe ensures your business has access to the most reliable data to excel in a rapidly evolving industry.

Platforms and Restaurants Covered: MealMe's database includes data from leading restaurant platforms such as UberEats, Postmates, ToastTakeout, SkipTheDishes, Square, Appfront, Olo, TouchBistro, and Clover, as well as direct menu data from major restaurant chains including Raising Cane’s, Panda Express, Popeyes, Burger King, and Subway. This extensive coverage ensures a detailed view of the market, helping businesses monitor trends, pricing, and availability across a broad spectrum of restaurant types and sizes.

Key Features: Comprehensive Menu Data: Access detailed menu information, including item descriptions, categories, sizes, and customizations. Real-Time Pricing: Monitor up-to-date menu prices for accurate competitive analysis. Restaurant-Specific Insights: Analyze individual restaurant chains such as Raising Cane’s and Panda Express, or platforms like UberEats, for market trends and pricing strategies. Cross-Platform Analysis: Compare menu items and pricing across platforms like ToastTakeout, Olo, and SkipTheDishes for a holistic industry view. Regional Data: Understand geographic variations in menu offerings and pricing across the USA and Canada.

Use Cases: Competitive Intelligence: Track menu offerings, pricing strategies, and seasonal trends across platforms like UberEats and Postmates or chains like Popeyes and Subway. Market Research: Identify gaps in the market by analyzing menus and pricing from top restaurants. Pricing Optimization: Use real-time pricing data to inform dynamic pricing strategies and promotions. Trend Monitoring: Stay ahead by tracking popular menu items, regional preferences, and emerging food trends. Platform Analysis: Assess how restaurants perform across delivery platforms such as SkipTheDishes, Olo, and Square. Industries Benefiting from Our Data Restaurant Chains: Optimize menu offerings and pricing strategies with detailed competitor data. Food Delivery Platforms: Benchmark menu pricing and availability across competitive platforms. Market Research Firms: Conduct detailed analyses to identify opportunities and market trends. AI & Analytics Companies: Power recommendation engines and predictive models with robust menu data. Consumer Apps: Enhance app experiences with accurate menu and pricing data. Data Delivery and Integration

MealMe offers flexible integration options to ensure seamless access to our comprehensive menu data. Whether you need bulk exports for in-depth research or real-time updates via API, our solutions are designed to scale with your business needs.

Why Choose MealMe? Extensive Coverage: Menu data from 100,000+ restaurants, including major chains like Burger King and Raising Cane’s. Real-Time Accuracy: Up-to-date pricing and menu details for actionable insights. Customizable Solutions: Tailored datasets to meet your specific business objectives. Proven Expertise: Trusted by top companies for delivering reliable, actionable data. MealMe empowers businesses with the data needed to thrive in a competitive restaurant and food delivery market. For more information or to request a demo, contact us today!

Local county health departments inspect restaurants and other retail food service establishments to make sure that employees follow safe food handling practices and have adequate kitchen facilities. Keep in mind, inspection reports are snapshots of the food handling at the establishment at the time of inspection – conditions may be different when you visit.

This information is derived from inspections of restaurants and other food establishments in Chicago from January 1, 2010 to the present. Inspections are performed by staff from the Chicago Department of Public Health’s Food Protection Program using a standardized procedure. The results of the inspection are inputted into a database, then reviewed and approved by a State of Illinois Licensed Environmental Health Practitioner (LEHP). For descriptions of the data elements included in this set, go to http://bit.ly/tS9IE8
Disclaimer: Attempts have been made to minimize any and all duplicate inspection reports. However, the dataset may still contain such duplicates and the appropriate precautions should be exercised when viewing or analyzing these data. The result of the inspections (pass, pass with conditions or fail) as well as the violations noted are based on the findings identified and reported by the inspector at the time of the inspection, and may not reflect the findings noted at other times. For more information about Food Inspections, go to http://bit.ly/tD91Sb. Data Owner: Chicago Department of Public Health. Time Period: 2010 - Present. Frequency: Data is updated weekly.

Restaurant email list is a database of contact information for professionals working in the Restaurant. These lists are created for businesses that want to sell their products or services to Restaurant. Instead of general contacts, these lists often focus on key decision-makers who are responsible for purchasing. There are, such as general managers, Restaurant owners, directors of sales or marketing, and directors of food and beverage. Moreover, you don’t have to search for contacts. A pre-built list saves time. This lets your sales team focus on building relationships and closing deals. Five star restaurants often have booking systems on their websites. This makes it simple for guests to choose and reserve a room.

Restaurant email list is crucial for any business that sells to the hospitality industry. You can contact people likely to buy your product. This includes software, cleaning supplies, or amenities. You can send specific messages to them. This leads to better results and more interest. In short, this lead is a powerful tool for efficiently and effectively connecting with the hospitality industry. This helps people book rooms even when they are on the go. Resorts, which have many activities, should highlight these on their websites. So, get it now from our website, List to Data. Restaurant email database is a list that shows the phone numbers of all restaurant companies. This resource is just what you need. This special list includes verified phone numbers of all nationals across global, giving you direct access to this important group. Also, this isn’t just any list; it’s carefully made to help you get the best results.

Restaurant email database can create marketing messages that speak directly to the community. This makes your efforts more successful and helps build trust with your audience. It’s also great for businesses looking to form partnerships, find investors, or explore new markets. Don’t waste time on general marketing that doesn’t work.

This map shows the average amount spent on meals away from home at restaurants or other per household in the U.S. in 2020 in a multiscale map (by country, state, county, ZIP Code, tract, and block group).The pop-up is configured to include the following information for each geography level:Average annual spending on meals at restaurants per householdAverage annual spending on all food away from home per householdAverage annual spending on food by meal typeThis map shows Esri's 2020 U.S. Consumer Spending Data in Census 2010 geographies. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data.Esri's 2020 U.S. Consumer Spending database provides the details about which products and services consumers buy, including total dollars spent, average amount spent per household, and a Spending Potential Index. Esri's Consumer Spending database identifies hundreds of items in more than 15 categories, including apparel, food and beverage, financial, entertainment and recreation, and household goods and services. See Consumer Spending database to view the methodology statement and complete variable list.Additional Esri Resources:Esri DemographicsU.S. 2020/2025 Esri Updated DemographicsEssential demographic vocabularyPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Extensive US food and dining POI database with 943K locations. Covers restaurants, dessert parlors, bakeries, cafes, and more. Ideal for market analysis, trend spotting, and strategic planning in the food service industry. High-quality data enables confident business decisions.

Context

This dataset has been obtained by scraping TA (the famous tourism website) for information about restaurants for a given city. The scraper goes through the restaurants listing pages and fulfills a raw dataset. The raw datasets for the main cities in Europe have been then curated for futher analysis purposes, and aggregated to obtain this dataset.

The scraper is a Python script, available on the GitHub repository here.

It uses principally pandas and BeautifulSoup libraries.

IMPORTANT: the restaurants list contains the restaurants that are registrered in the TA database only. All the restaurants of a city may not be resgistered in this database.

Content

The dataset contain restaurants information for 31 cities in Europe: Amsterdam (NL), Athens (GR) , Barcelona (ES) , Berlin (DE), Bratislava (SK), Bruxelles (BE), Budapest (HU), Copenhagen (DK), Dublin (IE), Edinburgh (UK), Geneva (CH), Helsinki (FI), Hamburg (DE), Krakow (PL), Lisbon (PT), Ljubljana (SI), London (UK), Luxembourg (LU), Madrid (ES), Lyon (FR), Milan (IT), Munich (DE), Oporto (PT), Oslo (NO), Paris (FR), Prague (CZ), Rome (IT), Stockholm (SE), Vienna (AT), Warsaw (PL), Zurich (CH).

The data is a .csv file comma-separated that contains 125 433 entries (restaurants). It is structured as follow: - Name: name of the restaurant

• City: city location of the restaurant

• Cuisine Style: cuisine style(s) of the restaurant, in a Python list object (94 046 non-null)

• Ranking: rank of the restaurant among the total number of restaurants in the city as a float object (115 645 non-null)

• Rating: rate of the restaurant on a scale from 1 to 5, as a float object (115 658 non-null)

• Price Range: price range of the restaurant among 3 categories , as a categorical type (77 555 non-null)

• Number of Reviews: number of reviews that customers have let to the restaurant, as a float object (108 020 non-null)

• Reviews: 2 reviews that are displayed on the restaurants scrolling page of the city, as a list of list object where the first list contains the 2 reviews, and the second le dates when these reviews were written (115 673 non-null)

• URL_TA: part of the URL of the detailed restaurant page that comes after 'www.tripadvisor.com' as a string object (124 995 non-null)

• ID_TA: identification of the restaurant in the TA database constructed a one letter and a number (124 995 non-null)

Missing information for restaurants (for example unrated or unreviewed restaurants) are in the dataset as NaN (numpy.nan).

Acknowledgements

This work has been done as a personal interest but also as a training of the skills I got from the DataCamp data science bootcamp I have followed.

I hope you will find this dataset inspiring and will make great stories out of it that I will be pleased to read :)

Xavvy fuel is the leading source for location data and market insights worldwide. We specialize in data quality and enrichment, providing high-quality POI data for restaurants and quick-service establishments in the United States.

Base data • Name/Brand • Adress • Geocoordinates • Opening Hours • Phone • ... ^

30+ Services • Delivery • Wifi • ChargePoints • …

10+ Payment options • Visa • MasterCard • Google Pay • individual Apps • ...

Our data offering is highly customizable and flexible in delivery – whether one-time or regular data delivery, push or pull services, and various data formats – we adapt to our customers' needs.

Brands included: • McDonalds • Burger King • Subway • KFC • Wendy's • ...

The total number of restaurants per region, market share distribution among competitors, or the ideal location for new branches – our restaurant data provides valuable insights into the food service market and serves as the perfect foundation for in-depth analyses and statistics. Our data helps businesses across various industries make informed decisions regarding market development, expansion, and competitive strategies. Additionally, our data contributes to the consistency and quality of existing datasets. A simple data mapping allows for accuracy verification and correction of erroneous entries.

Especially when displaying information about restaurants and fast-food chains on maps or in applications, high data quality is crucial for an optimal customer experience. Therefore, we continuously optimize our data processing procedures: • Regular quality controls • Geocoding systems to refine location data • Cleaning and standardization of datasets • Consideration of current developments and mergers • Continuous expansion and cross-checking of various data sources

Integrate the most comprehensive database of restaurant locations in the USA into your business. Explore our additional data offerings and gain valuable market insights directly from the experts!

Comprehensive dataset containing 2,225 verified Restaurant businesses in Idaho, United States with complete contact information, ratings, reviews, and location data.

This data is related to the spatial distribution study of restaurants and cafes in Surabaya City. There are two operational data: 1) spatial data in *shp, shx, prj, cpg, qpj, dbf format, which can be opened using GIS software; and 2) statistical data in *omv format, opened using Jamovi software.

Comprehensive dataset containing 180 verified Doner kebab restaurant businesses in United States with complete contact information, ratings, reviews, and location data.

The dataset, Survey-SR, provides the nutrient data for assessing dietary intakes from the national survey What We Eat In America, National Health and Nutrition Examination Survey (WWEIA, NHANES). Historically, USDA databases have been used for national nutrition monitoring (1). Currently, the Food and Nutrient Database for Dietary Studies (FNDDS) (2), is used by Food Surveys Research Group, ARS, to process dietary intake data from WWEIA, NHANES. Nutrient values for FNDDS are based on Survey-SR. Survey-SR was referred to as the "Primary Data Set" in older publications. Early versions of the dataset were composed mainly of commodity-type items such as wheat flour, sugar, milk, etc. However, with increased consumption of commercial processed and restaurant foods and changes in how national nutrition monitoring data are used (1), many commercial processed and restaurant items have been added to Survey-SR. The current version, Survey-SR 2013-2014, is mainly based on the USDA National Nutrient Database for Standard Reference (SR) 28 (2) and contains sixty-six nutrientseach for 3,404 foods. These nutrient data will be used for assessing intake data from WWEIA, NHANES 2013-2014. Nutrient profiles were added for 265 new foods and updated for about 500 foods from the version used for the previous survey (WWEIA, NHANES 2011-12). New foods added include mainly commercially processed foods such as several gluten-free products, milk substitutes, sauces and condiments such as sriracha, pesto and wasabi, Greek yogurt, breakfast cereals, low-sodium meat products, whole grain pastas and baked products, and several beverages including bottled tea and coffee, coconut water, malt beverages, hard cider, fruit-flavored drinks, fortified fruit juices and fruit and/or vegetable smoothies. Several school lunch pizzas and chicken products, fast-food sandwiches, and new beef cuts were also added, as they are now reported more frequently by survey respondents. Nutrient profiles were updated for several commonly consumed foods such as cheddar, mozzarella and American cheese, ground beef, butter, and catsup. The changes in nutrient values may be due to reformulations in products, changes in the market shares of brands, or more accurate data. Examples of more accurate data include analytical data, market share data, and data from a nationally representative sample. Resources in this dataset:Resource Title: USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES 2013-14 (Survey SR 2013-14). File Name: SurveySR_2013_14 (1).zipResource Description: Access database downloaded on November 16, 2017. US Department of Agriculture, Agricultural Research Service, Nutrient Data Laboratory. USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES (Survey-SR), October 2015. Resource Title: Data Dictionary. File Name: SurveySR_DD.pdf

This data set is sunset and will not be updating any more. Please go to this link for updated information: https://inspections.myhealthdepartment.com/dallas

This data set is intended to communicate the name of establishment, the physical location of the establishment, the date the inspection was conducted, the overall score for the inspection, and the point deduction for the individual violations.

Disclaimer: The inspection data represents a specific period in time. It does not represent the ownership of the establishment or the full history of the establishment.

This is a mutli-modal dataset for restaurants from Google Local (Google Maps). Data includes images and reviews posted by users, as well as metadata for each restaurant.

This map layer shows the average amount spent on meals away from home at restaurants or other per household in the U.S. in 2016 in a multiscale map (by country, state, county, ZIP Code, tract, and block group).The pop-up is configured to include the following information for each geography level:Average annual spending for meals at restaurants per householdAverage annual spending on all food away from home per householdAverage annual spending for food by meal typeThis map shows Esri's 2016 U.S. Consumer Spending Data in Census 2010 geographies. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data.Esri's 2016 U.S. Consumer Spending database provides the details about which products and services consumers buy, including total dollars spent, average amount spent per household, and a Spending Potential Index. Esri's Consumer Spending database identifies hundreds of items in more than 15 categories, including apparel, food and beverage, financial, entertainment and recreation, and household goods and services. See Consumer Spending database to view the methodology statement and complete variable list.Additional Esri Resources:Esri DemographicsU.S. 2016/2021 Esri Updated DemographicsEssential demographic vocabulary

Comprehensive dataset containing 1,391 verified Italian restaurant businesses in Massachusetts, United States with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 61 verified Soup restaurant businesses in Alabama, United States with complete contact information, ratings, reviews, and location data.

Comprehensive dataset of 2 Traditional restaurants in Nebraska, United States as of August, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset containing 1,046 verified Hamburger restaurant businesses in South Carolina, United States with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 61 verified Sushi restaurant businesses in Idaho, United States with complete contact information, ratings, reviews, and location data.