electricsheepafrica/africa-aviation-south-africa

Airports in South Africa

Publisher: OurAirports · Source: HDX · License: Public Domain · Updated: 2026-04-17

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Abstract

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

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-04-17. Geographic scope: ZAF.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	First-level administrative unit observations
Rows (total)	675
Columns	20 (6 numeric, 13 categorical, 0 datetime)
Train split	540 rows
Test split	135 rows
Geographic scope	ZAF
Publisher	OurAirports
HDX last updated	2026-04-17

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Variables

Geographic — type (small_airport, medium_airport, heliport), latitude_deg (range -46.876–-22.3557), longitude_deg (range 16.5333–37.8574), country_name (South Africa, #country +name), iso_country (ZA, #country +code +iso2) and 4 others.

Temporal — last_updated.

Outcome / Measurement — score (range 0.0–51275.0).

Identifier / Metadata — id (range 2767.0–607148.0), ident (#meta +code, ZA-0188, ZA-KIG), name (Benics Airport, #loc +airport +name, Braehead Airport), gps_code, esa_source and 1 others.

Other — elevation_ft (range 10.0–6464.0), continent (AF, #region +continent +code), scheduled_service (range 0.0–1.0).

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Quick Start

from datasets import load_dataset

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

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
id	float64	0.1%	2767.0 – 607148.0 (mean 159425.0356)
ident	object	0.0%	#meta +code, ZA-0188, ZA-KIG
type	object	0.0%	small_airport, medium_airport, heliport
name	object	0.0%	Benics Airport, #loc +airport +name, Braehead Airport
latitude_deg	float64	0.1%	-46.876 – -22.3557 (mean -28.5529)
longitude_deg	float64	0.1%	16.5333 – 37.8574 (mean 26.3706)
elevation_ft	float64	12.1%	10.0 – 6464.0 (mean 3086.4857)
continent	object	0.0%	AF, #region +continent +code
country_name	object	0.0%	South Africa, #country +name
iso_country	object	0.0%	ZA, #country +code +iso2
region_name	object	0.0%	Northern Cape, Eastern Cape, Limpopo
iso_region	object	0.0%	ZA-NC, ZA-EC, ZA-NP
local_region	object	0.0%	NC, EC, NP
municipality	object	5.2%	Cape Town, Johannesburg, Durban
scheduled_service	float64	0.1%	0.0 – 1.0 (mean 0.0341)
gps_code	object	53.3%
score	float64	0.1%	0.0 – 51275.0 (mean 160.2374)
last_updated	datetime64[ns, UTC]	0.1%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
id	2767.0	607148.0	159425.0356	41238.5
latitude_deg	-46.876	-22.3557	-28.5529	-28.3367
longitude_deg	16.5333	37.8574	26.3706	27.4971
elevation_ft	10.0	6464.0	3086.4857	3455.0
scheduled_service	0.0	1.0	0.0341	0.0
score	0.0	51275.0	160.2374	50.0

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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. 6 column(s) with >80% missing values were removed: icao_code, iata_code, local_code, home_link, wikipedia_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.

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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: gps_code.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_aviation_south_africa,
  title     = {Airports in South Africa},
  author    = {OurAirports},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/ourairports-zaf},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.