electricsheepafrica/africa-ourairports-ago

Airports in Angola

Publisher: OurAirports · Source: HDX · License: cc-by-igo · Updated: 2026-02-19

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Abstract

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

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-02-19. Geographic scope: AGO.

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)	127
Columns	23 (6 numeric, 16 categorical, 0 datetime)
Train split	101 rows
Test split	25 rows
Geographic scope	AGO
Publisher	OurAirports
HDX last updated	2026-02-19

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Variables

Geographic — type (small_airport, medium_airport, closed), latitude_deg (range -18.0208–-5.3956), longitude_deg (range 11.7222–23.5537), country_name (Angola, #country +name), iso_country (AO, #country +code +iso2) and 4 others.

Temporal — last_updated.

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

Identifier / Metadata — id (range 9.0–601203.0), ident (#meta +code, FNUA, AO-0049), name (Luena Airport, Jamba Airport, Maquela Airport), gps_code, icao_code and 3 others.

Other — elevation_ft (range 10.0–5778.0), continent (AF, #region +continent +code), scheduled_service (range 0.0–1.0), wikipedia_link.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-ourairports-ago")
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	0.8%	9.0 – 601203.0 (mean 209438.6032)
ident	object	0.0%	#meta +code, FNUA, AO-0049
type	object	0.0%	small_airport, medium_airport, closed
name	object	0.0%	Luena Airport, Jamba Airport, Maquela Airport
latitude_deg	float64	0.8%	-18.0208 – -5.3956 (mean -12.2089)
longitude_deg	float64	0.8%	11.7222 – 23.5537 (mean 17.0176)
elevation_ft	float64	40.9%	10.0 – 5778.0 (mean 2787.0)
continent	object	0.0%	AF, #region +continent +code
country_name	object	0.0%	Angola, #country +name
iso_country	object	0.0%	AO, #country +code +iso2
region_name	object	0.0%	Cuando Cubango Province, Moxico Province, Benguela Province
iso_region	object	0.0%	AO-CCU, AO-MOX, AO-BGU
local_region	object	0.0%	CCU, MOX, BGU
municipality	object	0.0%	Chitato, Cuangar, Saurimo
scheduled_service	float64	0.8%	0.0 – 1.0 (mean 0.1111)
gps_code	object	57.5%
icao_code	object	60.6%
iata_code	object	65.4%
wikipedia_link	object	59.1%
score	float64	0.8%	0.0 – 1000.0 (mean 135.3175)
last_updated	datetime64[ns, UTC]	0.8%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
id	9.0	601203.0	209438.6032	318476.5
latitude_deg	-18.0208	-5.3956	-12.2089	-12.5006
longitude_deg	11.7222	23.5537	17.0176	16.4367
elevation_ft	10.0	5778.0	2787.0	3477.0
scheduled_service	0.0	1.0	0.1111	0.0
score	0.0	1000.0	135.3175	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. 3 column(s) with >80% missing values were removed: 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.

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

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Citation

@dataset{hdx_africa_ourairports_ago,
  title     = {Airports in Angola},
  author    = {OurAirports},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/ourairports-ago},
  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.