electricsheepafrica/africa-aviation-burkina-faso

Airports in Burkina Faso

Publisher: OurAirports · Source: HDX · License: Public Domain · Updated: 2026-04-15

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Abstract

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

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-04-15. Geographic scope: BFA.

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)	52
Columns	23 (6 numeric, 16 categorical, 0 datetime)
Train split	41 rows
Test split	10 rows
Geographic scope	BFA
Publisher	OurAirports
HDX last updated	2026-04-15

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Variables

Geographic — type (small_airport, large_airport, closed), latitude_deg (range 9.883–14.7909), longitude_deg (range -5.35–1.7846), country_name (Burkina Faso, #country +name), iso_country (BF, #country +code +iso2) and 4 others.

Temporal — last_updated.

Outcome / Measurement — score (range 50.0–1000.0).

Identifier / Metadata — id (range 2088.0–597081.0), ident (#meta +code, DFFD, DFCK), name (#loc +airport +name, Ouagadougou Thomas Sankara International Airport, Koudougou Airport), gps_code, icao_code and 3 others.

Other — elevation_ft (range 699.0–1706.0), 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-aviation-burkina-faso")
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	1.9%	2088.0 – 597081.0 (mean 46965.8431)
ident	object	0.0%	#meta +code, DFFD, DFCK
type	object	0.0%	small_airport, large_airport, closed
name	object	0.0%	#loc +airport +name, Ouagadougou Thomas Sankara International Airport, Koudougou Airport
latitude_deg	float64	1.9%	9.883 – 14.7909 (mean 12.2922)
longitude_deg	float64	1.9%	-5.35 – 1.7846 (mean -1.688)
elevation_ft	float64	1.9%	699.0 – 1706.0 (mean 1007.1176)
continent	object	0.0%	AF, #region +continent +code
country_name	object	0.0%	Burkina Faso, #country +name
iso_country	object	0.0%	BF, #country +code +iso2
region_name	object	0.0%	Oudalan Province, Tapoa Province, Comoe Province
iso_region	object	0.0%	BF-OUD, BF-TAP, BF-COM
local_region	object	0.0%	OUD, TAP, COM
municipality	object	0.0%	#loc +municipality +name, Ouagadougou, Koudougou
scheduled_service	float64	1.9%	0.0 – 1.0 (mean 0.0392)
gps_code	object	3.8%
icao_code	object	28.8%
iata_code	object	46.2%
wikipedia_link	object	26.9%
score	float64	1.9%	50.0 – 1000.0 (mean 87.2549)
last_updated	datetime64[ns, UTC]	1.9%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
id	2088.0	597081.0	46965.8431	30908.0
latitude_deg	9.883	14.7909	12.2922	12.2
longitude_deg	-5.35	1.7846	-1.688	-1.6247
elevation_ft	699.0	1706.0	1007.1176	984.0
scheduled_service	0.0	1.0	0.0392	0.0
score	50.0	1000.0	87.2549	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: 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_aviation_burkina_faso,
  title     = {Airports in Burkina Faso},
  author    = {OurAirports},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/ourairports-bfa},
  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.