| --- |
| 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. |
|
|
