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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`](https://github.com/gabfssilva/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
```python
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`](https://data.mendeley.com/datasets/8kcdkxmcjw/1) — 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`](https://data.mendeley.com/datasets/cphdw4z5kw/2) — 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)`:
```python
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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