The motivation to assemble these historical data was to learn more about wine’s globalization. Some of the world's leading wine economists and historians have contributed to and drawn on this database to examine national wine market developments before, during and in between the 19th century and current waves of globalization. Their initial analyses cover all key wine-producing and wine-consuming countries using a common methodology to explain long-term trends and cycles in national wine production, consumption, and trade. More information about the database, the data sources and the methodology can be found on the Annual Database of Global Wine Markets web page.

Unlock the power of data with our comprehensive Total Wine Alcohol Products Dataset. Featuring detailed information on a wide range of wines, spirits, and beers, this dataset is perfect for data analysis, market research, and enhancing your product database.

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Analysis of ‘Classifying wine varieties’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/brynja/wineuci on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

Wine recognition dataset from UC Irvine. Great for testing out different classifiers

Labels: "name" - Number denoting a specific wine class

Number of instances of each wine class

• Class 1 - 59
• Class 2 - 71
• Class 3 - 48

Features:

1. Alcohol
2. Malic acid
3. Ash
4. Alcalinity of ash
5. Magnesium
6. Total phenols
7. Flavanoids
8. Nonflavanoid phenols
9. Proanthocyanins
10. Color intensity
11. Hue
12. OD280/OD315 of diluted wines
13. Proline

Content

"This data set is the result of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines"

Acknowledgements

Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

@misc{Lichman:2013 , author = "M. Lichman", year = "2013", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" }

UC Irvine data base: "https://archive.ics.uci.edu/ml/machine-learning-databases/wine"

Sources: (a) Forina, M. et al, PARVUS - An Extendible Package for Data Exploration, Classification and Correlation. Institute of Pharmaceutical and Food Analysis and Technologies, Via Brigata Salerno, 16147 Genoa, Italy. (b) Stefan Aeberhard, email: stefan@coral.cs.jcu.edu.au (c) July 1991 Past Usage: (1) S. Aeberhard, D. Coomans and O. de Vel, Comparison of Classifiers in High Dimensional Settings, Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of Mathematics and Statistics, James Cook University of North Queensland. (Also submitted to Technometrics).

The data was used with many others for comparing various classifiers. The classes are separable, though only RDA has achieved 100% correct classification. (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) (All results using the leave-one-out technique)

(2) S. Aeberhard, D. Coomans and O. de Vel, "THE CLASSIFICATION PERFORMANCE OF RDA" Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of Mathematics and Statistics, James Cook University of North Queensland. (Also submitted to Journal of Chemometrics).

Inspiration

This data set is great for drawing comparisons between algorithms and testing out classifications models when learning new techniques

--- Original source retains full ownership of the source dataset ---

Context

Wine recognition dataset from UC Irvine. Great for testing out different classifiers

Labels: "name" - Number denoting a specific wine class

Number of instances of each wine class

• Class 1 - 59
• Class 2 - 71
• Class 3 - 48

Features:

1. Alcohol
2. Malic acid
3. Ash
4. Alcalinity of ash
5. Magnesium
6. Total phenols
7. Flavanoids
8. Nonflavanoid phenols
9. Proanthocyanins
10. Color intensity
11. Hue
12. OD280/OD315 of diluted wines
13. Proline

Content

"This data set is the result of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines"

Acknowledgements

Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

@misc{Lichman:2013 , author = "M. Lichman", year = "2013", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" }

UC Irvine data base: "https://archive.ics.uci.edu/ml/machine-learning-databases/wine"

Sources: (a) Forina, M. et al, PARVUS - An Extendible Package for Data Exploration, Classification and Correlation. Institute of Pharmaceutical and Food Analysis and Technologies, Via Brigata Salerno, 16147 Genoa, Italy. (b) Stefan Aeberhard, email: stefan@coral.cs.jcu.edu.au (c) July 1991 Past Usage: (1) S. Aeberhard, D. Coomans and O. de Vel, Comparison of Classifiers in High Dimensional Settings, Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of Mathematics and Statistics, James Cook University of North Queensland. (Also submitted to Technometrics).

The data was used with many others for comparing various classifiers. The classes are separable, though only RDA has achieved 100% correct classification. (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) (All results using the leave-one-out technique)

(2) S. Aeberhard, D. Coomans and O. de Vel, "THE CLASSIFICATION PERFORMANCE OF RDA" Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of Mathematics and Statistics, James Cook University of North Queensland. (Also submitted to Journal of Chemometrics).

Inspiration

This data set is great for drawing comparisons between algorithms and testing out classifications models when learning new techniques

Digital Wine Cellar App Market Outlook

According to our latest research, the global Digital Wine Cellar App market size reached USD 1.34 billion in 2024, with robust momentum driven by digital transformation in the wine industry. The market is expected to expand at a CAGR of 11.2% during the forecast period, reaching USD 3.08 billion by 2033. This growth is primarily attributed to increasing smartphone penetration, the rising sophistication of wine consumers, and the growing demand for digital inventory management among individual collectors and businesses. As per our analysis, the proliferation of innovative features and seamless integration with smart devices are further accelerating market adoption globally.

The growth trajectory of the Digital Wine Cellar App market is underpinned by several key factors. First, the increasing digitization of lifestyle choices has significantly influenced how wine enthusiasts, collectors, and retailers manage their collections and experiences. With consumers seeking more personalized and data-driven wine experiences, these apps offer functionalities like inventory management, tasting notes, and food pairing suggestions, which are highly valued by both novices and connoisseurs. The convenience of having a comprehensive wine management solution at one’s fingertips, combined with real-time access to global wine databases and community-driven reviews, is enhancing user engagement and driving market expansion.

Another crucial growth driver is the rapid adoption of mobile technology across emerging and developed markets. The ubiquity of smartphones and improved internet connectivity have made Digital Wine Cellar Apps more accessible to a broader demographic. Additionally, the integration of artificial intelligence and machine learning algorithms into these platforms enables advanced features such as personalized wine recommendations and predictive analytics for inventory optimization. The growing trend of social sharing and community engagement within these apps also appeals to younger wine consumers, fostering a sense of belonging and boosting app downloads and subscriptions across regions.

Moreover, the increasing collaboration between app developers and wine industry stakeholders, including vineyards, retailers, and hospitality businesses, is fostering innovation in app functionality. Restaurants and bars are leveraging these platforms to streamline their wine inventory and enhance the customer experience through tailored wine lists and pairing suggestions. Meanwhile, wine retailers are utilizing app-based analytics to better understand consumer preferences and optimize their offerings. This ecosystem approach is not only expanding the addressable market but also driving continuous improvement in app features, thereby sustaining long-term market growth.

Regionally, North America and Europe dominate the Digital Wine Cellar App market, accounting for a significant share of global revenues. These regions benefit from a mature wine culture, high disposable incomes, and advanced technological infrastructure. However, the Asia Pacific region is emerging as a lucrative market, driven by the rising popularity of wine among younger consumers and increasing smartphone adoption. Latin America and the Middle East & Africa are also witnessing gradual growth, supported by expanding urbanization and growing interest in wine culture. This regional diversification is expected to further stimulate market growth over the forecast period.

Platform Analysis

The Digital Wine Cellar App market is segmented by platform into iOS, Android, and Web-based solutions, each catering to distinct user preferences and technological ecosystems. The iOS segment has traditionally held a dominant position, particularly in North America and Europe, where Apple devices are prevalent among affluent consumers and wine enthusiasts. iOS-based apps are often perceived as offering superior design, enhanced security, and seamless integration with other Apple products, making them the preferred choice for premium app developers targeting high-value users. This segment continues to benefit from Apple’s strong brand loyalty and the willingness of iOS users to spend on paid and subscription-based services.

The Android segment, however, is rapidly gaining traction, especially in emerging markets across Asia Pacific and Latin America. The widespread ad

Theoretic conjecture and mathematical models used for classifying wines into different chromatic levels were examined, based on a comprehensive chromatic database of 237 dry red wines. Similar mathematical models were also built for 79 Chinese Cabernet Sauvignon dry red wines from different regions and vintages, which could be set as chromatic reference for other Chinese Cabernet Sauvignon dry red wines. The reliability of these models and relationships between different chromatic parameters were further validated by statistical tools. Such methodology can be adopted to build chromatic references for wines from different backgrounds, thus to classify wine color more extensively and objectively.

Context

There are two datasets with different details. Depending on these details, the quality of the wine is found

Content

The datasets are uploaded from http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Comprehensive dataset of 32,862 Wine stores in United States as of July, 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 of 41 Wines in United States as of July, 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.

The Wine Economics Research Centre has produced this latest edition of global wine statistics, in a major revision, update and backdate of the preceding issue of the Global Wine Markets statistical compendium.

The following files can be downloaded from the Compendium web page:
Front pages - list of tables, technical notes, abbreviations, statistical sources, and authors’ preface. [pdf 28 pages, 2477kB]

Charts: Global wine markets at a glance [pdf 28 pages, 2477kB]

Tables:
I. Global wine markets, 2014-16 [pdf 7 pages, 228kB; Excel 82kB]

II. Wine markets by country: annual data, 2006 to 2016 [pdf 120 pages, 7207kB; Excel 2370kB]

III. Wine markets by country: decadal data, 1860s to 2016 [pdf 173 pages, 7937kB; Excel 1184kB]

IV. Wine bilateral trade, country by region, 1990 to 2016 [pdf 85 pages, 4790kB; Excel 874kB]

V. Wine bilateral trade, country by country, 2016 [pdf 57 pages, 1839kB; Excel 440kB]

VI. Wine and other alcohol consumption taxes, 2008, 2012 and 2014 [pdf 4 pages, 145kB; Excel 20kB]

VII. Wine and other (tax-inclusive) retail beverage consumption expenditure, 2001 to 2015 [pdf 26 pages, 2980kB; Excel 593kB]

VIII. Indexes of intensity and similarity in alcohol consumption volume, by region, 1961 to 2015 [pdf 11 pages, 635kB; Excel 133kB]

IX. Indexes of intensity, similarity and quality of alcohol consumption, by country, 2001 to 2015 [pdf 14 pages, 1558kB; Excel 299kB]

X. Earlier total and bilateral wine trade data and alcohol taxes, 1323 to 1940 [pdf 19 pages, 720kB; Excel 126kB]

Comprehensive dataset of 14,948 Wine bars in 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.

Our book entitled 'Growth and Cycles in Australia's Wine Industry: A Statistical Compendium, 1843 to 2013' is a compilation of annual data on the economic history of the development of the grape and wine industry in Australia. The e-book version may be downloaded free of charge from the University of Adelaide Press, where a hard copy also may be ordered.

The underlying data are available to freely download. Please acknowledge the database source as: Anderson, K. and N. Aryal, Australian Grape and Wine Industry Database, 1843 to 2013, Wine Economics Research Centre, University of Adelaide, February 2015.

An Executive Summary of Key Findings and the Introduction chapter ('Front pages') of the e-book, and nearly 100 summary charts, are available as three separate files, as are the four sections of tables from the e-book in PDF and Excel formats, plus a more-detailed fifth section of tables of annual regional by varietal data from 1999 to 2013.

Wines: Approved for Circulation: AOC & VDQS: Dept: Lot-Et-Garonne data was reported at 13,223.000 hl in Apr 2018. This records an increase from the previous number of 13,000.000 hl for Mar 2018. Wines: Approved for Circulation: AOC & VDQS: Dept: Lot-Et-Garonne data is updated monthly, averaging 11,405.000 hl from Aug 2002 (Median) to Apr 2018, with 189 observations. The data reached an all-time high of 41,699.000 hl in Oct 2005 and a record low of 1,475.000 hl in Oct 2008. Wines: Approved for Circulation: AOC & VDQS: Dept: Lot-Et-Garonne data remains active status in CEIC and is reported by General Directorate of Customs and Excise. The data is categorized under Global Database’s France – Table FR.B013: Wine Statistics.

.WINE Whois Database, discover comprehensive ownership details, registration dates, and more for .WINE TLD with Whois Data Center.

China Other Wine: Product Inventory data was reported at 1.161 RMB bn in Oct 2015. This records an increase from the previous number of 1.113 RMB bn for Sep 2015. China Other Wine: Product Inventory data is updated monthly, averaging 0.536 RMB bn from Dec 2003 (Median) to Oct 2015, with 97 observations. The data reached an all-time high of 1.161 RMB bn in Oct 2015 and a record low of 0.167 RMB bn in Dec 2004. China Other Wine: Product Inventory data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BHC: Wine: Other Wine.

Comprehensive dataset of 3,089 Wine stores in California, United States as of July, 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.

United States Imports from Spain of Vermouth, Other Similar Wine (Flavoured) was US$2.07 Million during 2024, according to the United Nations COMTRADE database on international trade. United States Imports from Spain of Vermouth, Other Similar Wine (Flavoured) - data, historical chart and statistics - was last updated on August of 2025.

China Other Wine: YoY: Product Inventory data was reported at 9.342 % in Oct 2015. This records a decrease from the previous number of 13.292 % for Sep 2015. China Other Wine: YoY: Product Inventory data is updated monthly, averaging 14.610 % from Jan 2006 (Median) to Oct 2015, with 89 observations. The data reached an all-time high of 56.962 % in Nov 2012 and a record low of -5.850 % in Nov 2009. China Other Wine: YoY: Product Inventory data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BHC: Wine: Other Wine.

China Other Wine: Loss Amount: Year to Date data was reported at 0.043 RMB bn in Oct 2015. This records an increase from the previous number of 0.036 RMB bn for Sep 2015. China Other Wine: Loss Amount: Year to Date data is updated monthly, averaging 0.010 RMB bn from Dec 2003 (Median) to Oct 2015, with 97 observations. The data reached an all-time high of 0.043 RMB bn in Oct 2015 and a record low of 0.000 RMB bn in Nov 2011. China Other Wine: Loss Amount: Year to Date data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BHC: Wine: Other Wine.

Comprehensive dataset of 442 Wine stores in Colorado, United States as of July, 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.