Wine Quality

uci:wine-quality@1 · Registry 1.2.0. Default version in this registry.

Physicochemical and sensory-quality measurements for red and white vinho verde wines distributed by the UCI Machine Learning Repository.

Dimensions and tasks

Task	Type	Split	Samples	Predictors
default	regression	—	6,497	11

Predictor counts follow each task's feature selection and exclude targets and identifiers. Sample counts are reported separately for each split.

Retrieve

These examples pin both the dataset version and the registry's exact index. An uncached dataset requires network access to its source. The CLI retrieves verified artifacts. The language clients also decode them.

Python

from datamonger import Registry, fetch_data

registry = Registry(
    release="1.2.0",
    index_sha256="4b4e6aeb71ad7e53c3596a725e62008c76c27a59ac68c53a02432898ec8b6f21",
    index_url="https://github.com/jolars/datamonger/releases/download/registry-v1.2.0/index.json",
)
data = fetch_data("uci:wine-quality@1", registry=registry)

R

library(datamonger)

registry <- registry_selector(
  release = "1.2.0",
  index_sha256 = "4b4e6aeb71ad7e53c3596a725e62008c76c27a59ac68c53a02432898ec8b6f21",
  index_url = "https://github.com/jolars/datamonger/releases/download/registry-v1.2.0/index.json"
)
data <- fetch_data("uci:wine-quality@1", registry = registry)

Julia

using Datamonger

registry = registry_selector(
    "1.2.0",
    "4b4e6aeb71ad7e53c3596a725e62008c76c27a59ac68c53a02432898ec8b6f21",
    "https://github.com/jolars/datamonger/releases/download/registry-v1.2.0/index.json",
)
data = fetch_data("uci:wine-quality@1"; registry=registry)

CLI

cat > datamonger-registry.json <<'JSON'
{
  "index_sha256": "4b4e6aeb71ad7e53c3596a725e62008c76c27a59ac68c53a02432898ec8b6f21",
  "index_url": "https://github.com/jolars/datamonger/releases/download/registry-v1.2.0/index.json",
  "release": "1.2.0",
  "schema_version": 1
}
JSON
datamonger --selector datamonger-registry.json fetch 'uci:wine-quality@1'

Representation and variables

Tabular data with a dense representation. Decoder: delimited-text version 1.

Component	Kind	Type	Shape
fixed_acidity	vector	float64	6,497
volatile_acidity	vector	float64	6,497
citric_acid	vector	float64	6,497
residual_sugar	vector	float64	6,497
chlorides	vector	float64	6,497
free_sulfur_dioxide	vector	float64	6,497
total_sulfur_dioxide	vector	float64	6,497
density	vector	float64	6,497
pH	vector	float64	6,497
sulphates	vector	float64	6,497
alcohol	vector	float64	6,497
quality	vector	int64	6,497
color	vector	string	6,497

Preprocessing

Preprocessing history is not recorded in this registry release.

Provenance and citation

Provider: UCI Machine Learning Repository. Upstream name: Wine Quality. Retrieved: 2026-09-06.

Original source

Authors: Paulo Cortez, A. Cerdeira, F. Almeida, T. Matos, J. Reis.

Cortez, P., Cerdeira, A., Almeida, F., Matos, T., and Reis, J. (2009). Wine Quality. UCI Machine Learning Repository. https://doi.org/10.24432/C56S3T.

License

CC-BY-4.0

License information

The UCI dataset page states that the dataset is licensed under CC BY 4.0.

Artifacts

Total download: 381.4 KiB. Sizes describe the distributed files, including any compression.

Artifact	Format	Compression	Size	Distribution	Preservation
data	csv	none	381.4 KiB	upstream-only	unknown

data: upstream

Versions in this registry

1 (default)

Registry record

Download the complete registry record, registry selector, or full registry index.

Index SHA-256: 4b4e6aeb71ad7e53c3596a725e62008c76c27a59ac68c53a02432898ec8b6f21.