README.md

metadata

license: mit
task_categories:
  - tabular-classification
  - tabular-regression
language:
  - en
size_categories:
  - n<1K
  - 1K<n<10K
tags:
  - imputation
  - missing-data
  - tabular
pretty_name: imputify datasets
configs:
  - config_name: iris
    data_files: iris.parquet
  - config_name: wine
    data_files: wine.parquet
  - config_name: diabetes
    data_files: diabetes.parquet
  - config_name: breast_cancer
    data_files: breast_cancer.parquet
  - config_name: titanic
    data_files: titanic.parquet
  - config_name: heart_disease
    data_files: heart_disease.parquet
  - config_name: blood_transfusion
    data_files: blood_transfusion.parquet
  - config_name: thalassemia
    data_files: thalassemia.parquet
  - config_name: ilpd
    data_files: ilpd.parquet
  - config_name: spas_agri
    data_files: spas_agri.parquet

imputify datasets

Curated tabular datasets bundled with the imputify library for examples, tests, and missing-data benchmarks. Every file is a single parquet: feature columns first, target column last, no missing values. Column names are snake_case; columns that the upstream sources include for identification but not modeling (IDs, free-text names) have been dropped.

Usage

from imputify import load, introduce_missing

X, y = load("iris")                              # clean
X_missing, mask = introduce_missing(X, 0.3)      # ampute for experiments

load caches downloads through huggingface_hub (default ~/.cache/huggingface/hub). The complete catalogue is exposed as imputify.DATASETS and the literal type as imputify.Dataset.

Catalogue

Name	Rows	Features	Numeric	Categorical	Target dtype	Original source
iris	150	4	4	0	int64	sklearn.datasets.load_iris (Fisher, 1936)
wine	178	13	13	0	int64	sklearn.datasets.load_wine (UCI Wine Recognition)
diabetes	442	10	10	0	float64	sklearn.datasets.load_diabetes (Efron et al., 2004)
breast_cancer	569	30	30	0	int64	sklearn.datasets.load_breast_cancer (UCI WDBC)
titanic	1 043	7	5	2	category	OpenML titanic
heart_disease	270	13	13	0	category	UCI Heart Disease / OpenML heart-statlog
blood_transfusion	748	4	4	0	category	UCI Blood Transfusion Service Center
thalassemia	606	18	12	6	object	Mendeley Data 8kcdkxmcjw — Pabna, Bangladesh thalassemia cohort, Data in Brief 2025
ilpd	583	10	9	1	category	UCI Indian Liver Patient Dataset (ILPD)
spas_agri	4 191	14	9	5	object	Mendeley Data cphdw4z5kw — SPAS-Dataset-BD, Bangladesh precision agriculture, Data in Brief 2025

Counts come from running the loader once: rows = len(X), features = X.shape[1], numeric/categorical split = select_dtypes(...).

Format

Each parquet has the target as the last column. imputify.load(name) splits it back into (X, y):

df = pd.read_parquet("iris.parquet")
X, y = df.drop(columns=["target"]), df["target"]

Categorical features are stored as pandas category (titanic) or object (thalassemia, spas_agri) and round-trip through parquet without manual casting.

Licensing & attribution

Datasets carry their original licences. iris, wine, diabetes, and breast_cancer ship with scikit-learn (BSD-compatible). The UCI / OpenML datasets are redistributed under their respective terms. The thalassemia and spas_agri derivatives are from Mendeley Data (CC BY 4.0); cite the Data in Brief 2025 papers in publications.