electricsheepasia/asia-health-facilities-afghanistan-health-facilities

Afghanistan - Health Facilities

Publisher: OCHA Afghanistan · Source: HDX · License: cc-by · Updated: 2025-09-16

Abstract

Number of health facilities and their status of functionality by province.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-09-16. Geographic scope: AFG.

Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).

Dataset Characteristics

Variables

Geographic — sehatmandi_health_facilities_functionality_status_table_as_of_20_september_2021 (range 1.0–34.0).

Identifier / Metadata — unnamed_1 (Central , South, Northern), unnamed_2 (Province, Kandahar, Hemlmand), unnamed_3 (range 18.0–2312.0), unnamed_4 (range 0.0–393.0), unnamed_5 (range 0.0–1819.0) and 3 others.

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-health-facilities-afghanistan-health-facilities")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Numeric Summary

Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snakecase. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 1 column(s) with >80% missing values were removed: `unnamed7`. 5 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

Limitations

• —Data originates from OCHA Afghanistan and has not been independently validated by ESA.
• —Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• —Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_asia_health_facilities_afghanistan_health_facilities,
  title     = {Afghanistan - Health Facilities},
  author    = {OCHA Afghanistan},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/afghanistan-health-facilities},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.