electricsheepafrica/africa-nigeria-health-care-facilities-in-nigeria

---
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
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- facilities-infrastructure
- health-facilities
- nga
pretty_name: "Nigeria: Health Care Facilities in Nigeria"
dataset_info:
splits:
- name: train
num_examples: 36916
- name: test
num_examples: 9229
---

# Nigeria: Health Care Facilities in Nigeria

**Publisher:** HDX · **Source:** [HDX](https://data.humdata.org/dataset/nigeria-health-care-facilities-in-nigeria) · **License:** `cc-by` · **Updated:** 2025-03-10

---

## Abstract

Nigeria country-wide primary, secondary, and tertiary health care facility points and names and functional status

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2025-03-10. 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** | 26 (8 numeric, 16 categorical, 0 datetime) |
| **Train split** | 36,916 rows |
| **Test split** | 9,229 rows |
| **Geographic scope** | NGA |
| **Publisher** | HDX |
| **HDX last updated** | 2025-03-10 |

---

## Variables

**Geographic** — `x` (range 2.7078–14.6364), `y` (range 4.2818–13.8652), `latitude` (range 4.2818–13.8652), `longitude` (range 2.7078–14.6364), `wardname` (Urban 1, Township, Opeilu / Ibaragun) and 9 others.

**Temporal** — `timestamp`, `updated_on`.

**Identifier / Metadata** — `fid` (range 1.0–46146.0), `globalid` (af719462-abfd-4f47-9dc3-0987164e75ac, ded60d55-c90a-40c1-8dc0-d02267cec30a, 31896c26-8b37-4080-8be7-6145d748ef4d), `uniq_id` (range 1.0–46608.0), `source`, `alt_name` and 2 others.

**Other** — `editor` (tosin.williams, mokobia.chidinma, najib.adam), `func_stats` (Functional, Unknown, Not Functional), `ownership` (State Primary Healthcare Development Agency, National Primary Healthcare Development Agency, Others).

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 0.0% | 2.7078 – 14.6364 (mean 7.2414) |
| `y` | float64 | 0.0% | 4.2818 – 13.8652 (mean 8.4048) |
| `fid` | int64 | 0.0% | 1.0 – 46146.0 (mean 23073.5) |
| `globalid` | object | 0.0% | af719462-abfd-4f47-9dc3-0987164e75ac, ded60d55-c90a-40c1-8dc0-d02267cec30a, 31896c26-8b37-4080-8be7-6145d748ef4d |
| `uniq_id` | int64 | 0.0% | 1.0 – 46608.0 (mean 23311.1242) |
| `timestamp` | datetime64[ns, UTC] | 0.0% | |
| `editor` | object | 0.0% | tosin.williams, mokobia.chidinma, najib.adam |
| `latitude` | float64 | 0.0% | 4.2818 – 13.8652 (mean 8.4048) |
| `longitude` | float64 | 0.0% | 2.7078 – 14.6364 (mean 7.2414) |
| `wardname` | object | 0.0% | Urban 1, Township, Opeilu / Ibaragun |
| `wardcode` | float64 | 85.6% | 10101.0 – 81411.0 (mean 49898.8456) |
| `lganame` | object | 0.0% | Municipal Area Council, Ifo, Surulere |
| `lgacode` | int64 | 0.0% | 101.0 – 35016.0 (mean 15658.7492) |
| `statename` | object | 0.0% | Lagos, Katsina, Benue |
| `statecode` | object | 2.4% | LA, KT, BE |
| `updated_on` | datetime64[ns, UTC] | 0.5% | |
| `accessblty` | object | 0.0% | , Unknown, Yes |
| `func_stats` | object | 0.0% | Functional, Unknown, Not Functional |
| `category` | object | 0.0% | Primary Health Center, Dispensary, Maternity Home |
| `ownership` | object | 0.0% | State Primary Healthcare Development Agency, National Primary Healthcare Development Agency, Others |
| `type` | object | 0.0% | |
| `source` | object | 0.0% | |
| `alt_name` | object | 0.0% | |
| `prmry_name` | object | 0.0% | |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | 2.7078 | 14.6364 | 7.2414 | 7.3019 |
| `y` | 4.2818 | 13.8652 | 8.4048 | 7.7257 |
| `fid` | 1.0 | 46146.0 | 23073.5 | 23073.5 |
| `uniq_id` | 1.0 | 46608.0 | 23311.1242 | 23315.5 |
| `latitude` | 4.2818 | 13.8652 | 8.4048 | 7.7257 |
| `longitude` | 2.7078 | 14.6364 | 7.2414 | 7.3019 |
| `wardcode` | 10101.0 | 81411.0 | 49898.8456 | 50409.0 |
| `lgacode` | 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`. 3 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: `wardcode`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/nigeria-health-care-facilities-in-nigeria) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_nigeria_health_care_facilities_in_nigeria,
title = {Nigeria: Health Care Facilities in Nigeria},
author = {HDX},
year = {2025},
url = {https://data.humdata.org/dataset/nigeria-health-care-facilities-in-nigeria},
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.*