Gabriel Francisco dos Santos Silva
Attribute thalassemia/spas_agri to Mendeley Data
d11fa72 verified | 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. | |