Datasets:

gabfssilva
/

imputify-datasets

recency int64 0 74	frequency int64 1 50	monetary int64 250 12.5k	time int64 2 98	target stringclasses 2 values
2	50	12,500	98	2
0	13	3,250	28	2
1	16	4,000	35	2
2	20	5,000	45	2
1	24	6,000	77	1
4	4	1,000	4	1
2	7	1,750	14	2
1	12	3,000	35	1
2	9	2,250	22	2
5	46	11,500	98	2
4	23	5,750	58	1
0	3	750	4	1
2	10	2,500	28	2
1	13	3,250	47	1
2	6	1,500	15	2
2	5	1,250	11	2
2	14	3,500	48	2
2	15	3,750	49	2
2	6	1,500	15	2
2	3	750	4	2
2	3	750	4	2
4	11	2,750	28	1
2	6	1,500	16	2
2	6	1,500	16	2
9	9	2,250	16	1
4	14	3,500	40	1
4	6	1,500	14	1
4	12	3,000	34	2
4	5	1,250	11	2
4	8	2,000	21	1
1	14	3,500	58	1
4	10	2,500	28	2
4	10	2,500	28	2
4	9	2,250	26	2
2	16	4,000	64	1
2	8	2,000	28	2
2	12	3,000	47	2
4	6	1,500	16	2
2	14	3,500	57	2
4	7	1,750	22	2
2	13	3,250	53	2
2	5	1,250	16	1
2	5	1,250	16	2
2	5	1,250	16	1
4	20	5,000	69	2
4	9	2,250	28	2
2	9	2,250	36	1
2	2	500	2	1
2	2	500	2	1
2	2	500	2	1
2	11	2,750	46	1
2	11	2,750	46	2
2	6	1,500	22	1
2	12	3,000	52	1
4	5	1,250	14	2
4	19	4,750	69	2
4	8	2,000	26	2
2	7	1,750	28	2
2	16	4,000	81	1
3	6	1,500	21	1
2	7	1,750	29	1
2	8	2,000	35	2
2	10	2,500	49	1
4	5	1,250	16	2
2	3	750	9	2
3	16	4,000	74	1
2	4	1,000	14	2
0	2	500	4	1
4	7	1,750	25	1
1	9	2,250	51	1
2	4	1,000	16	1
2	4	1,000	16	1
4	17	4,250	71	2
2	2	500	4	1
2	2	500	4	2
2	2	500	4	2
2	4	1,000	16	2
2	2	500	4	1
2	2	500	4	1
2	2	500	4	1
4	6	1,500	23	2
2	4	1,000	16	1
2	4	1,000	16	1
2	4	1,000	16	1
2	6	1,500	28	2
2	6	1,500	28	1
4	2	500	4	1
4	2	500	4	1
4	2	500	4	1
2	7	1,750	35	2
4	2	500	4	2
4	2	500	4	1
4	2	500	4	1
4	2	500	4	1
12	11	2,750	23	1
4	7	1,750	28	1
3	17	4,250	86	1
4	9	2,250	38	2
4	4	1,000	14	2
5	7	1,750	26	2

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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.

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