electricsheepafrica/africa-aviation-zambia

Airports in Zambia

Publisher: OurAirports · Source: HDX · License: Public Domain · Updated: 2026-04-09

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Abstract

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

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-04-09. Geographic scope: ZMB.

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)	133
Columns	21 (7 numeric, 13 categorical, 0 datetime)
Train split	106 rows
Test split	26 rows
Geographic scope	ZMB
Publisher	OurAirports
HDX last updated	2026-04-09

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Variables

Geographic — type (small_airport, medium_airport, closed), latitude_deg (range -18.0192–-8.4318), longitude_deg (range 22.417–33.4062), country_name (Zambia, #country +name), iso_country (ZM, #country +code +iso2) and 4 others.

Temporal — last_updated.

Outcome / Measurement — score (range 0.0–1050.0).

Identifier / Metadata — id (range 2891.0–607162.0), ident (#meta +code, ZM-0024, ZM-0027), name (#loc +airport +name, Majuaneti Airport, Mufumbwe Airstrip), gps_code (#loc +airport +code +gps, FLMA, FLMP), icao_code and 2 others.

Other — elevation_ft (range 1165.0–5454.0), 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-zambia")
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%	2891.0 – 607162.0 (mean 180444.7273)
ident	object	0.0%	#meta +code, ZM-0024, ZM-0027
type	object	0.0%	small_airport, medium_airport, closed
name	object	0.0%	#loc +airport +name, Majuaneti Airport, Mufumbwe Airstrip
latitude_deg	float64	0.8%	-18.0192 – -8.4318 (mean -14.1044)
longitude_deg	float64	0.8%	22.417 – 33.4062 (mean 27.9469)
elevation_ft	float64	17.3%	1165.0 – 5454.0 (mean 3513.2545)
continent	object	0.0%	AF, #region +continent +code
country_name	object	0.0%	Zambia, #country +name
iso_country	object	0.0%	ZM, #country +code +iso2
region_name	object	0.0%	Southern Province, North-Western Province, Lusaka Province
iso_region	object	0.0%	ZM-07, ZM-06, ZM-09
local_region	float64	1.5%	1.0 – 10.0 (mean 5.7557)
municipality	object	3.8%	Livingstone, Kitwe, Zimba
scheduled_service	float64	0.8%	0.0 – 1.0 (mean 0.053)
gps_code	object	36.1%	#loc +airport +code +gps, FLMA, FLMP
icao_code	object	68.4%
score	float64	0.8%	0.0 – 1050.0 (mean 92.0455)
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	2891.0	607162.0	180444.7273	31815.5
latitude_deg	-18.0192	-8.4318	-14.1044	-14.1177
longitude_deg	22.417	33.4062	27.9469	28.1042
elevation_ft	1165.0	5454.0	3513.2545	3685.0
local_region	1.0	10.0	5.7557	6.0
scheduled_service	0.0	1.0	0.053	0.0
score	0.0	1050.0	92.0455	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. 5 column(s) with >80% missing values were removed: iata_code, local_code, home_link, wikipedia_link, keywords. 8 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, icao_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_zambia,
  title     = {Airports in Zambia},
  author    = {OurAirports},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/ourairports-zmb},
  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.