These data are the results 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.

Dataset

This dataset was created by Saeful Abdulloh Sayuti

Contents

Dataset

This dataset was created by Fazle Rabbi

Contents

These data are the results of a chemical analysis of wines made out of grapes grown in a region in Italy. But it is derived from three different cultivators. The analysis determined the quantities of 13 constituents found in each of the three types of wines.

A compendium of data on wine and grape production in winegrape bearing regions in Australia. There are four sheets that record data by region: 1) wine variables; 2) yield for 2006 and 2008; 3) time series data from 1999-2008; and 4) data on water usage by state. The data include, for example, statistics on grape and wine employment and value of grape and wine output.

Dataset to be attributed to The University of Adelaide.

This dataset contains a collection of wine reviews and comments provided by various wine tasters. Its primary purpose is to facilitate data exploration and text analytics, particularly for the recognition and classification of individual commenters based on their review styles. It is an excellent resource for those new to Natural Language Processing (NLP) and is designed to demonstrate key techniques involved in language processing, operating on the assumption that each taster possesses a unique descriptive style for wines.

Columns

• Unnamed: An unlabelled column, likely an index or ID.
• country: Specifies the country of origin for the wine, covering countries from around the world.
• description: Contains the detailed reviews and comments given for each wine variety.
• designation: Provides the wine's designation.
• points: Represents the score achieved by the wine variety, as awarded by the reviewer.
• price: Indicates the price of the wine.
• province: Denotes the province where the wine originates.
• region_1: Specifies the first region associated with the wine.
• region_2: Specifies the second region associated with the wine.
• taster_name: The name of the individual taster who provided the review.

Distribution

The dataset typically comes in CSV format and comprises approximately 130,000 records. * Country Distribution: The United States accounts for 42% of entries, France 17%, with other countries making up 41%. * Points Distribution: Scores range from 80 to 100, with the majority falling between 86.00 and 89.20. * Price Distribution: Prices vary significantly, with a large concentration (over 110,000 records) in the 4.00-69.92 range, extending up to 3300. * Province Distribution: California is the most represented province at 28%, followed by Washington at 7%, and others at 65%. * Taster Distribution: Approximately 20% of entries have no taster name recorded, while Roger Voss accounts for 20% of the named tasters. * Certain columns like 'designation', 'region_1', and 'region_2' contain a notable percentage of null values (29%, 16%, and 61% respectively).

Usage

This dataset is ideal for various applications and use cases, including: * Data exploration and initial data analysis. * Text analytics for understanding patterns in wine reviews. * Text classification to categorise or identify wine tasters based on their review content. * Demonstrating and learning Natural Language Processing (NLP) techniques. * Developing models for multiclass classification.

Coverage

The dataset has a global geographic scope, featuring wines from various countries, with a significant presence from the United States (42%) and France (17%). Key provinces such as California (28%) and Washington (7%) are well-represented. No specific time range for the reviews is provided in the available information. The demographic scope centres around the named wine tasters, though no detailed demographic information about them is included. Data availability varies by column, with some columns containing a considerable number of missing values.

License

CC0

Who Can Use It

• Beginners in Natural Language Processing (NLP) looking for a practical dataset to apply text classification techniques.
• Data scientists and analysts interested in performing data exploratory analysis on text data.
• Researchers focusing on text analytics and authorship attribution (recognising commenters).
• Anyone aiming to build and demonstrate models for language processing or multiclass classification.

Dataset Name Suggestions

• Wine Reviews & Taster Comments
• Global Wine Tasting Notes
• Wine Review NLP Dataset
• Wine Taster Classification Data
• Wine Ratings and Descriptions

Attributes

Original Data Source: Winedata

Dataset

This dataset was created by Sadab Ali

Released under Apache 2.0

Contents

"These data are the results 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.

I think that the initial data set had around 30 variables, but for some reason I only have the 13 dimensional version. I had a list of what the 30 or so variables were, but a.) I lost it, and b.), I would not know which 13 variables are included in the set.

The attributes are (dontated by Riccardo Leardi, riclea '@' anchem.unige.it ) 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

In a classification context, this is a well posed problem with ""well behaved"" class structures. A good data set for first testing of a new classifier, but not very challenging."

Dataset

This dataset was created by Vedanth Subramaniam

Contents

Citation Request: This dataset is public available for research. The details are described in [Cortez et al., 2009]. Please include this citation if you plan to use this database:

P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.

Available at: [@Elsevier] http://dx.doi.org/10.1016/j.dss.2009.05.016 [Pre-press (pdf)] http://www3.dsi.uminho.pt/pcortez/winequality09.pdf [bib] http://www3.dsi.uminho.pt/pcortez/dss09.bib

Title: Wine Quality Sources Created by: Paulo Cortez (Univ. Minho), Antonio Cerdeira, Fernando Almeida, Telmo Matos and Jose Reis (CVRVV) @ 2009 Past Usage:

P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.

In the above reference, two datasets were created, using red and white wine samples. The inputs include objective tests (e.g. PH values) and the output is based on sensory data (median of at least 3 evaluations made by wine experts). Each expert graded the wine quality between 0 (very bad) and 10 (very excellent). Several data mining methods were applied to model these datasets under a regression approach. The support vector machine model achieved the best results. Several metrics were computed: MAD, confusion matrix for a fixed error tolerance (T), etc. Also, we plot the relative importances of the input variables (as measured by a sensitivity analysis procedure). Relevant Information:

The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine. For more details, consult: http://www.vinhoverde.pt/en/ or the reference [Cortez et al., 2009]. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.).

These datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are munch more normal wines than excellent or poor ones). Outlier detection algorithms could be used to detect the few excellent or poor wines. Also, we are not sure if all input variables are relevant. So it could be interesting to test feature selection methods. Number of Instances: red wine - 1599; white wine - 4898. Number of Attributes: 11 + output attribute

Note: several of the attributes may be correlated, thus it makes sense to apply some sort of feature selection. Attribute information:

For more information, read [Cortez et al., 2009].

Input variables (based on physicochemical tests): 1 - fixed acidity (tartaric acid - g / dm^3) 2 - volatile acidity (acetic acid - g / dm^3) 3 - citric acid (g / dm^3) 4 - residual sugar (g / dm^3) 5 - chlorides (sodium chloride - g / dm^3 6 - free sulfur dioxide (mg / dm^3) 7 - total sulfur dioxide (mg / dm^3) 8 - density (g / cm^3) 9 - pH 10 - sulphates (potassium sulphate - g / dm3) 11 - alcohol (% by volume) Output variable (based on sensory data): 12 - quality (score between 0 and 10) Missing Attribute Values: None Description of attributes:

1 - fixed acidity: most acids involved with wine or fixed or nonvolatile (do not evaporate readily)

2 - volatile acidity: the amount of acetic acid in wine, which at too high of levels can lead to an unpleasant, vinegar taste

3 - citric acid: found in small quantities, citric acid can add 'freshness' and flavor to wines

4 - residual sugar: the amount of sugar remaining after fermentation stops, it's rare to find wines with less than 1 gram/liter and wines with greater than 45 grams/liter are considered sweet

5 - chlorides: the amount of salt in the wine

6 - free sulfur dioxide: the free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved gas) and bisulfite ion; it prevents microbial growth and the oxidation of wine

7 - total sulfur dioxide: amount of free and bound forms of S02; in low concentrations, SO2 is mostly undetectable in wine, but at free SO2 concentrations over 50 ppm, SO2 becomes evident in the nose and taste of wine

8 - density: the density of water is close to that of water depending on the percent alcohol and sugar content

9 - pH: describes how acidic or basic a wine is on a scale from 0 (very acidic) to 14 (very basic); most wines are between 3-4 on the pH scale

10 - sulphates: a wine additive which can contribute to sulfur dioxide gas (S02) levels, wich acts as an antimicrobial and antioxidant

11 - alcohol: the percent alcohol content of the wine

Output variable (based on sensory data): 12 - quality (score between 0 and 10)

United States CPI U: FB: AB: Away from Home: Wine data was reported at 187.319 Dec1997=100 in Jun 2018. This records an increase from the previous number of 186.112 Dec1997=100 for May 2018. United States CPI U: FB: AB: Away from Home: Wine data is updated monthly, averaging 150.201 Dec1997=100 from Dec 1997 (Median) to Jun 2018, with 244 observations. The data reached an all-time high of 187.319 Dec1997=100 in Jun 2018 and a record low of 100.000 Dec1997=100 in Dec 1997. United States CPI U: FB: AB: Away from Home: Wine data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I002: Consumer Price Index: Urban.

Analysis of ‘Wine Dataset for Clustering’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/harrywang/wine-dataset-for-clustering on 30 September 2021.

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

This dataset is adapted from the Wine Data Set from https://archive.ics.uci.edu/ml/datasets/wine by removing the information about the types of wine for unsupervised learning.

The following descriptions are adapted from the UCI webpage:

These data are the results 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.

The attributes are:

• Alcohol
• Malic acid
• Ash
• Alcalinity of ash
• Magnesium
• Total phenols
• Flavanoids
• Nonflavanoid phenols
• Proanthocyanins
• Color intensity
• Hue
• OD280/OD315 of diluted wines
• Proline

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

United States CPI U: AW: FB: AB: At Home: Wine data was reported at 0.239 % in 2017. This records a decrease from the previous number of 0.241 % for 2016. United States CPI U: AW: FB: AB: At Home: Wine data is updated yearly, averaging 0.225 % from Dec 1997 (Median) to 2017, with 21 observations. The data reached an all-time high of 0.254 % in 2005 and a record low of 0.175 % in 2001. United States CPI U: AW: FB: AB: At Home: Wine data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I011: Consumer Price Index: Urban: Weights (Annual).

United States CPI UW: FB: AB: At Home: Wine data was reported at 0.253 % in Jun 2018. This records an increase from the previous number of 0.252 % for May 2018. United States CPI UW: FB: AB: At Home: Wine data is updated monthly, averaging 0.227 % from Jan 1998 (Median) to Jun 2018, with 246 observations. The data reached an all-time high of 0.259 % in Feb 2006 and a record low of 0.174 % in May 2001. United States CPI UW: FB: AB: At Home: Wine data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I010: Consumer Price Index: Urban: Weights.

Exports of Wine in Germany decreased to 72665 EUR Thousand in January from 85510 EUR Thousand in December of 2023. This dataset includes a chart with historical data for Germany Exports of Wine.

Russia Retail Sales: AB: Year to Date: WP: Fruit Wine data was reported at 0.848 dal mn in Jan 2019. This records a decrease from the previous number of 14.247 dal mn for Dec 2018. Russia Retail Sales: AB: Year to Date: WP: Fruit Wine data is updated monthly, averaging 5.696 dal mn from Jan 2017 (Median) to Jan 2019, with 25 observations. The data reached an all-time high of 14.247 dal mn in Dec 2018 and a record low of 0.406 dal mn in Jan 2017. Russia Retail Sales: AB: Year to Date: WP: Fruit Wine data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.RJB016: Retail Sales: Alcohol.

Austria - Harmonised index of consumer prices (HICP): Wine was 118.73 points in May of 2025, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Austria - Harmonised index of consumer prices (HICP): Wine - last updated from the EUROSTAT on July of 2025. Historically, Austria - Harmonised index of consumer prices (HICP): Wine reached a record high of 120.89 points in March of 2025 and a record low of 73.60 points in December of 2000.

United States CPI U: FB: AB: At Home: Wine data was reported at 169.763 1982-1984=100 in Jun 2018. This records an increase from the previous number of 169.150 1982-1984=100 for May 2018. United States CPI U: FB: AB: At Home: Wine data is updated monthly, averaging 132.900 1982-1984=100 from Dec 1963 (Median) to Jun 2018, with 611 observations. The data reached an all-time high of 173.778 1982-1984=100 in Sep 2009 and a record low of 41.600 1982-1984=100 in Dec 1964. United States CPI U: FB: AB: At Home: Wine data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I002: Consumer Price Index: Urban.

Dataset

This dataset was created by Rumeysa K

Contents

Russia Retail Sales: AB: Year to Date: WP: Liquor Wine data was reported at 0.010 dal mn in Jan 2019. This records a decrease from the previous number of 0.172 dal mn for Dec 2018. Russia Retail Sales: AB: Year to Date: WP: Liquor Wine data is updated monthly, averaging 0.071 dal mn from Jan 2017 (Median) to Jan 2019, with 25 observations. The data reached an all-time high of 0.172 dal mn in Dec 2018 and a record low of 0.007 dal mn in Jan 2017. Russia Retail Sales: AB: Year to Date: WP: Liquor Wine data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.RJB016: Retail Sales: Alcohol.