--- license: cc-by-4.0 task_categories: - tabular-classification - tabular-regression language: - en tags: - healthcare - medicine-quality - online-pharmacy - e-pharmacy - social-media - darknet - falsified - consumer-safety - sub-saharan-africa - lmic pretty_name: "Online Pharmacy & E-Pharmacy Regulation (SF Prevalence, Platform Type, Consumer Safety)" size_categories: - 10K Validation Report

## Usage ```python from datasets import load_dataset dataset = load_dataset("electricsheepafrica/online-pharmacy-regulation", "social_media_marketplace") df = dataset["train"].to_pandas() print(df.groupby('platform_name')['quality_test_result'].apply(lambda x: (x=='fail').mean())) ``` ## References 1. WHO (2024). SF medicines sold online. 2. NABP (2023). 95% of online pharmacies non-compliant. 3. Interpol Operation Pangea. Illicit online pharmacy crackdowns. 4. LegitScript. 30,000+ rogue pharmacy websites. ## Citation ```bibtex @dataset{esa_epharmacy_2025, title = {Online Pharmacy and E-Pharmacy Regulation Dataset}, author = {{Electric Sheep Africa}}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/electricsheepafrica/online-pharmacy-regulation} } ``` ## License [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)