electricsheepafrica/africa-ourairports-swz

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- aviation
- facilities-infrastructure
- geodata
- hxl
- transportation
- swz
pretty_name: "Airports in Eswatini"
dataset_info:
splits:
- name: train
num_examples: 14
- name: test
num_examples: 3
---

# Airports in Eswatini

**Publisher:** OurAirports · **Source:** [HDX](https://data.humdata.org/dataset/ourairports-swz) · **License:** `cc-by-igo` · **Updated:** 2026-01-22

---

## Abstract

List of airports in Eswatini, with latitude and longitude. Unverified community data from http://ourairports.com/countries/SZ/

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-01-22. Geographic scope: **SWZ**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | First-level administrative unit observations |
| **Rows (total)** | 18 |
| **Columns** | 22 (6 numeric, 15 categorical, 0 datetime) |
| **Train split** | 14 rows |
| **Test split** | 3 rows |
| **Geographic scope** | SWZ |
| **Publisher** | OurAirports |
| **HDX last updated** | 2026-01-22 |

---

## Variables

**Geographic** — `type` (small_airport, large_airport, #loc +airport +type), `latitude_deg` (range -27.3201–-25.7981), `longitude_deg` (range 31.1439–31.9426), `country_name` (Eswatini, #country +name), `iso_country` (SZ, #country +code +iso2) and 4 others.

**Temporal** — `last_updated`.

**Outcome / Measurement** — `score` (range 0.0–1000.0).

**Identifier / Metadata** — `id` (range 2872.0–550112.0), `ident` (#meta +code, FDMS, FDTS), `name` (#loc +airport +name, Matsapha International Airport, Tshaneni Airport), `gps_code`, `icao_code` and 2 others.

**Other** — `elevation_ft` (range 500.0–3865.0), `continent` (AF, #region +continent +code), `scheduled_service` (range 0.0–1.0), `wikipedia_link`.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-ourairports-swz")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `id` | float64 | 5.6% | 2872.0 – 550112.0 (mean 77708.1176) |
| `ident` | object | 0.0% | #meta +code, FDMS, FDTS |
| `type` | object | 0.0% | small_airport, large_airport, #loc +airport +type |
| `name` | object | 0.0% | #loc +airport +name, Matsapha International Airport, Tshaneni Airport |
| `latitude_deg` | float64 | 5.6% | -27.3201 – -25.7981 (mean -26.5482) |
| `longitude_deg` | float64 | 5.6% | 31.1439 – 31.9426 (mean 31.6753) |
| `elevation_ft` | float64 | 11.1% | 500.0 – 3865.0 (mean 1373.875) |
| `continent` | object | 0.0% | AF, #region +continent +code |
| `country_name` | object | 0.0% | Eswatini, #country +name |
| `iso_country` | object | 0.0% | SZ, #country +code +iso2 |
| `region_name` | object | 0.0% | Lubombo Region, Shiselweni Region, Hhohho Region |
| `iso_region` | object | 0.0% | SZ-LU, SZ-SH, SZ-HH |
| `local_region` | object | 0.0% | LU, SH, HH |
| `municipality` | object | 0.0% | Big Bend, #loc +municipality +name, Manzini |
| `scheduled_service` | float64 | 5.6% | 0.0 – 1.0 (mean 0.1176) |
| `gps_code` | object | 5.6% | |
| `icao_code` | object | 22.2% | |
| `wikipedia_link` | object | 27.8% | |
| `score` | float64 | 5.6% | 0.0 – 1000.0 (mean 158.8235) |
| `last_updated` | datetime64[ns, UTC] | 5.6% | |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `id` | 2872.0 | 550112.0 | 77708.1176 | 31105.0 |
| `latitude_deg` | -27.3201 | -25.7981 | -26.5482 | -26.5289 |
| `longitude_deg` | 31.1439 | 31.9426 | 31.6753 | 31.81 |
| `elevation_ft` | 500.0 | 3865.0 | 1373.875 | 973.0 |
| `scheduled_service` | 0.0 | 1.0 | 0.1176 | 0.0 |
| `score` | 0.0 | 1000.0 | 158.8235 | 50.0 |

---

## 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`. 4 column(s) with >80% missing values were removed: `iata_code`, `local_code`, `home_link`, `keywords`. 7 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 OurAirports 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: `icao_code`, `wikipedia_link`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/ourairports-swz) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_ourairports_swz,
title = {Airports in Eswatini},
author = {OurAirports},
year = {2026},
url = {https://data.humdata.org/dataset/ourairports-swz},
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.*