README.md

---
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
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- health
- health-facilities
- gin
pretty_name: "Structures de Santé - Guinée"
dataset_info:
  splits:
    - name: train
      num_examples: 1949
    - name: test
      num_examples: 487
---

# Structures de Santé - Guinée

**Publisher:** American Red Cross (inactive) · **Source:** [HDX](https://data.humdata.org/dataset/structures_de_sante_guinee_vf) · **License:** `other-pd-nr` · **Updated:** 2025-02-06

---

## Abstract

Cette donnée contient la liste des structures de Santé de la Guinée. Cette donnée contient la liste de 2430 structures de santé dont 1773 avec les coordonnées géographiques (latitude et longitude). Le nombre de structures de santé sans coordonnées GPS est de 666.

Each row in this dataset represents geolocated point observations. Data was last updated on HDX on 2025-02-06. Geographic scope: **GIN**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Public health |
| **Unit of observation** | Geolocated point observations |
| **Rows (total)** | 2,437 |
| **Columns** | 3 (0 numeric, 3 categorical, 0 datetime) |
| **Train split** | 1,949 rows |
| **Test split** | 487 rows |
| **Geographic scope** | GIN |
| **Publisher** | American Red Cross (inactive) |
| **HDX last updated** | 2025-02-06 |

---

## Variables

**Geographic** — `nom_code_prefecture_latitude_longiture` (CSR de Albadariah centre;;CSR524;KISSIDOUGOU;;9.55066709;-10.09971425, PS Dandakara;;;DABOLA;;;, CSR de konendou;;CSR893;DABOLA;;10.68601728;-10.85957025).

**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-07).

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-structures-de-sante-guinee-vf")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `nom_code_prefecture_latitude_longiture` | object | 0.0% | CSR de Albadariah centre;;CSR524;KISSIDOUGOU;;9.55066709;-10.09971425, PS Dandakara;;;DABOLA;;;, CSR de konendou;;CSR893;DABOLA;;10.68601728;-10.85957025 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-07 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
_No numeric columns._

---

## 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`. 2 exact duplicate rows were removed. 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 American Red Cross (inactive) 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](https://data.humdata.org/dataset/structures_de_sante_guinee_vf) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_structures_de_sante_guinee_vf,
  title     = {Structures de Santé - Guinée},
  author    = {American Red Cross (inactive)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/structures_de_sante_guinee_vf},
  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.*