---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- tabular-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- health-facilities
- hxl
- eth
pretty_name: "Ethiopia Healthsites"
dataset_info:
  splits:
    - name: train
      num_examples: 892
    - name: test
      num_examples: 223
---

# Ethiopia Healthsites

**Publisher:** Global Healthsites Mapping Project · **Source:** [HDX](https://data.humdata.org/dataset/ethiopia-healthsites) · **License:** `ODbL` · **Updated:** 2025-10-15

---

## Abstract

This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-10-15. Geographic scope: **ETH**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Public health |
| **Unit of observation** | Tabular records |
| **Rows (total)** | 1,115 |
| **Columns** | 14 (6 numeric, 7 categorical, 0 datetime) |
| **Train split** | 892 rows |
| **Test split** | 223 rows |
| **Geographic scope** | ETH |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-10-15 |

---

## Variables

**Geographic** — `x` (range 34.2391–46.5313), `y` (range 3.4557–14.2886), `osm_type` (node, way), `amenity` (hospital, clinic, pharmacy).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range 42656243.0–13146838802.0), `name` (Solo Da Pharmacy, Health Center, Wojel Health Center), `changeset_id` (range 3394188.0–172004469.0), `uuid` (e04ec30211664b3fac0627c03bc2e367, 076df684db92445e9fe560aa50688c58, 4c176eb2efac4ee9b44814211bf5b491), `esa_source` (HDX) and 1 others.

**Other** — `completeness` (range 6.25–34.375), `healthcare` (hospital, pharmacy, clinic), `changeset_version` (range 1.0–9.0).

---

## Quick Start

```python
from datasets import load_dataset

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

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 29.3% | 34.2391 – 46.5313 (mean 38.7375) |
| `y` | float64 | 29.3% | 3.4557 – 14.2886 (mean 9.52) |
| `osm_id` | int64 | 0.0% | 42656243.0 – 13146838802.0 (mean 5248194832.8717) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | float64 | 0.0% | 6.25 – 34.375 (mean 10.3924) |
| `amenity` | object | 1.0% | hospital, clinic, pharmacy |
| `healthcare` | object | 66.0% | hospital, pharmacy, clinic |
| `name` | object | 37.1% | Solo Da Pharmacy, Health Center, Wojel Health Center |
| `changeset_id` | int64 | 0.0% | 3394188.0 – 172004469.0 (mean 94709134.8233) |
| `changeset_version` | int64 | 0.0% | 1.0 – 9.0 (mean 1.5901) |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.0% |  |
| `uuid` | object | 0.0% | e04ec30211664b3fac0627c03bc2e367, 076df684db92445e9fe560aa50688c58, 4c176eb2efac4ee9b44814211bf5b491 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-20 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | 34.2391 | 46.5313 | 38.7375 | 38.7431 |
| `y` | 3.4557 | 14.2886 | 9.52 | 9.0188 |
| `osm_id` | 42656243.0 | 13146838802.0 | 5248194832.8717 | 5671866221.0 |
| `completeness` | 6.25 | 34.375 | 10.3924 | 9.375 |
| `changeset_id` | 3394188.0 | 172004469.0 | 94709134.8233 | 85566006.0 |
| `changeset_version` | 1.0 | 9.0 | 1.5901 | 1.0 |

---

## Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 23 column(s) with >80% missing values were removed: `operator`, `source`, `speciality`, `operator_type`, `operational_status`, `opening_hours`.... 1 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 Global Healthsites Mapping Project and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- The following columns have >20% missing values and should be treated with caution in modelling: `x`, `y`, `healthcare`, `name`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/ethiopia-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_health_facilities_ethiopia,
  title     = {Ethiopia Healthsites},
  author    = {Global Healthsites Mapping Project},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/ethiopia-healthsites},
  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.*