electricsheepafrica/africa-nigeria-health-facilities

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- tabular-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- facilities-infrastructure
- geodata
- health
- health-facilities
- nga
pretty_name: "Nigeria: Health facilities"
dataset_info:
splits:
- name: train
num_examples: 36916
- name: test
num_examples: 9229
---

# Nigeria: Health facilities

**Publisher:** HDX · **Source:** [HDX](https://data.humdata.org/dataset/nigeria-health-facilities) · **License:** `cc-by` · **Updated:** 2025-04-10

---

## Abstract

Nigeria Health facilities. These are Primary, Secondary and Tertiary entities that provide medical and/or healthcare services and/or engage in the use generally of natural and/or artificial materials to create or dispense drugs.

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2025-04-10. Geographic scope: **NGA**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Public health |
| **Unit of observation** | Subnational administrative unit observations |
| **Rows (total)** | 46,146 |
| **Columns** | 15 (3 numeric, 11 categorical, 0 datetime) |
| **Train split** | 36,916 rows |
| **Test split** | 9,229 rows |
| **Geographic scope** | NGA |
| **Publisher** | HDX |
| **HDX last updated** | 2025-04-10 |

---

## Variables

**Geographic** — `type` (Primary, Secondary, Tertiary), `ward_code` (range 10101.0–81411.0), `category` (Primary Health Center, Dispensary, Maternity Home), `lga_name` (Ifo, Municipal Area Council, Surulere), `lga_code` (range 101.0–35016.0) and 2 others.

**Temporal** — `timestamp`.

**Identifier / Metadata** — `id` (range 1.0–46608.0), `name` (Police Clinic, Alheri Clinic, Sabon Gari Primary Health Center), `global_id` (af719462-abfd-4f47-9dc3-0987164e75ac, 9b7d5ddf-2d0c-47ab-baf5-5cb964eee952, c071fc95-2229-4f8c-9de7-5301bddbc150), `fid` (sv_health_facilities.fid--3185df38_17a314b4ee6_9a6, sv_health_facilities.fid--3185df38_17a326f927a_-7e34, sv_health_facilities.fid--3185df38_17a326f927a_-7e32), `esa_source` (HDX) and 1 others.

**Other** — `functional_status` (Functional, Unknown, Not Functional).

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `id` | int64 | 0.0% | 1.0 – 46608.0 (mean 23311.1242) |
| `name` | object | 0.0% | Police Clinic, Alheri Clinic, Sabon Gari Primary Health Center |
| `global_id` | object | 0.0% | af719462-abfd-4f47-9dc3-0987164e75ac, 9b7d5ddf-2d0c-47ab-baf5-5cb964eee952, c071fc95-2229-4f8c-9de7-5301bddbc150 |
| `functional_status` | object | 0.0% | Functional, Unknown, Not Functional |
| `type` | object | 0.0% | Primary, Secondary, Tertiary |
| `ward_code` | float64 | 85.6% | 10101.0 – 81411.0 (mean 49898.8456) |
| `category` | object | 0.1% | Primary Health Center, Dispensary, Maternity Home |
| `timestamp` | datetime64[ns, UTC] | 0.0% | |
| `lga_name` | object | 0.0% | Ifo, Municipal Area Council, Surulere |
| `lga_code` | int64 | 0.0% | 101.0 – 35016.0 (mean 15658.7492) |
| `state_code` | object | 2.4% | LA, KT, BE |
| `state_name` | object | 0.0% | Lagos, Katsina, Benue |
| `fid` | object | 0.0% | sv_health_facilities.fid--3185df38_17a314b4ee6_9a6, sv_health_facilities.fid--3185df38_17a326f927a_-7e34, sv_health_facilities.fid--3185df38_17a326f927a_-7e32 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `id` | 1.0 | 46608.0 | 23311.1242 | 23315.5 |
| `ward_code` | 10101.0 | 81411.0 | 49898.8456 | 50409.0 |
| `lga_code` | 101.0 | 35016.0 | 15658.7492 | 16002.0 |

---

## 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`. 2 column(s) with >80% missing values were removed: `alternate_name`, `accessibility`. 2 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 HDX 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: `ward_code`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/nigeria-health-facilities) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_nigeria_health_facilities,
title = {Nigeria: Health facilities},
author = {HDX},
year = {2025},
url = {https://data.humdata.org/dataset/nigeria-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.*