Wholesale Customers

Browse datasets

uci:wholesale-customers@1 · Registry 1.2.0. Default version in this registry.

Annual spending by wholesale-distributor clients across product categories, as distributed by the UCI Machine Learning Repository.

Dimensions and tasks

Task

Type

Split

Samples

Predictors

No task specified

—

—

440

Unknown

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:wholesale-customers@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:wholesale-customers@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:wholesale-customers@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:wholesale-customers@1'

Representation and variables

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

Component

Kind

Type

Shape

Channel

vector

int64

440

Region

vector

int64

440

Fresh

vector

int64

440

Milk

vector

int64

440

Grocery

vector

int64

440

Frozen

vector

int64

440

Detergents_Paper

vector

int64

440

Delicassen

vector

int64

440

Preprocessing

Preprocessing history is not recorded in this registry release.

Provenance and citation

Provider: UCI Machine Learning Repository. Upstream name: Wholesale customers. Retrieved: 2026-09-06.

Original source

Authors: Margarida Cardoso.

Cardoso, M. (2013). Wholesale customers. UCI Machine Learning Repository. https://doi.org/10.24432/C5030X.

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: 14.7 KiB. Sizes describe the distributed files, including any compression.

Artifact

Format

Compression

Size

Distribution

Preservation

data

csv

none

14.7 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.