electricsheepafrica/africa-nigeria-hospitals-and-clinics-with-registration-status-and-location

---
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
- hxl
- nga
pretty_name: "Nigeria: Hospitals and Clinics with registration status and Location"
dataset_info:
splits:
- name: train
num_examples: 33651
- name: test
num_examples: 8412
---

# Nigeria: Hospitals and Clinics with registration status and Location

**Publisher:** HDX · **Source:** [HDX](https://data.humdata.org/dataset/nigeria-hospitals-and-clinics-with-registration-status-and-location) · **License:** `cc-by` · **Updated:** 2025-04-10

---

## Abstract

Hospitals and Clinics with registration status and Location in Nigeria. This dataset has been publicly provided by the Nigeria Federal Ministry of Health on the NIGERIA Health Facility Registry (HFR) website

Each row in this dataset represents subnational administrative unit observations. Temporal coverage is indicated by the `start_date`, `created` column(s). 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)** | 42,064 |
| **Columns** | 18 (3 numeric, 12 categorical, 3 datetime) |
| **Train split** | 33,651 rows |
| **Test split** | 8,412 rows |
| **Geographic scope** | NGA |
| **Publisher** | HDX |
| **HDX last updated** | 2025-04-10 |

---

## Variables

**Geographic** — `state` (Lagos, Katsina, Benue), `lga` (Abuja Municipal Area Council, Alimosho, Karu), `ward` (Unknown, Gui, Karu Unknown), `facility_code` (23/02/1/1/1/0072, 25/10/1/1/1/0057, 24/03/1/1/1/0158), `facility_name` (Aged and Widow Clinic, Nasarawa Health Clinic, Alheri Clinic) and 3 others.

**Temporal** — `start_date`, `last_updated`.

**Identifier / Metadata** — `uid` (range 12346487.0–87654002.0), `esa_source`, `esa_processed`.

**Other** — `ownership` (Public, Private), `operation_status` (Operational, Closed (Temporary), Pending Operation), `registration_status` (Registered, Unknown, Not Applicable), `license_status` (Licensed, Unknown, Not Applicable), `created`.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-nigeria-hospitals-and-clinics-with-registration-status-and-location")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `state` | object | 0.0% | Lagos, Katsina, Benue |
| `lga` | object | 0.0% | Abuja Municipal Area Council, Alimosho, Karu |
| `ward` | object | 0.0% | Unknown, Gui, Karu Unknown |
| `uid` | float64 | 0.0% | 12346487.0 – 87654002.0 (mean 50178006.9182) |
| `facility_code` | object | 0.0% | 23/02/1/1/1/0072, 25/10/1/1/1/0057, 24/03/1/1/1/0158 |
| `facility_name` | object | 0.0% | Aged and Widow Clinic, Nasarawa Health Clinic, Alheri Clinic |
| `start_date` | datetime64[ns] | 17.5% | |
| `ownership` | object | 0.0% | Public, Private |
| `facility_level` | object | 0.0% | Primary, Secondary, Tertiary |
| `longitude` | float64 | 12.0% | 2.483 – 15.1316 (mean 7.3452) |
| `latitude` | float64 | 11.9% | 3.883 – 13.867 (mean 8.6012) |
| `operation_status` | object | 0.0% | Operational, Closed (Temporary), Pending Operation |
| `registration_status` | object | 0.6% | Registered, Unknown, Not Applicable |
| `license_status` | object | 0.6% | Licensed, Unknown, Not Applicable |
| `created` | datetime64[ns] | 0.0% | |
| `last_updated` | datetime64[ns] | 0.0% | |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `uid` | 12346487.0 | 87654002.0 | 50178006.9182 | 50365141.0 |
| `longitude` | 2.483 | 15.1316 | 7.3452 | 7.3997 |
| `latitude` | 3.883 | 13.867 | 8.6012 | 8.1253 |

---

## 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`. 1 column(s) with >80% missing values were removed: `reg_number`. 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 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.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/nigeria-hospitals-and-clinics-with-registration-status-and-location) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_nigeria_hospitals_and_clinics_with_registration_status_and_location,
title = {Nigeria: Hospitals and Clinics with registration status and Location},
author = {HDX},
year = {2025},
url = {https://data.humdata.org/dataset/nigeria-hospitals-and-clinics-with-registration-status-and-location},
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.*