electricsheepafrica/africa-hdx-hapi-operational-presence

HDX HAPI - Coordination & Context: Operational Presence

Publisher: HDX Humanitarian API Data · Source: HDX · License: cc-by-igo · Updated: 2026-04-14

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Abstract

This dataset contains data obtained from the HDX Humanitarian API (HDX HAPI), which provides standardized humanitarian indicators designed for seamless interoperability from multiple sources. The data facilitates automated workflows and visualizations to support humanitarian decision making. For more information, please see the HDX HAPI landing page and documentation.

Warnings typically indicate corrections have been made to the data or show things to look out for. Rows with only warnings are considered complete, and are made available via the API. Errors usually mean that the data is incomplete or unusable. Rows with any errors are not present in the API but are included here for transparency.

Each row in this dataset represents subnational administrative unit observations. Temporal coverage is indicated by the reference_period_start, reference_period_end column(s). Geographic scope: AFG, BFA, CMR, CAF, TCD, COL, COD, ETH, and 13 others.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Subnational administrative unit observations
Rows (total)	36,191
Columns	21 (1 numeric, 18 categorical, 2 datetime)
Train split	28,952 rows
Test split	7,238 rows
Geographic scope	AFG, BFA, CMR, CAF, TCD, COL, COD, ETH, and 13 others
Publisher	HDX Humanitarian API Data
HDX last updated	2026-04-14

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Variables

Geographic — location_code (AFG, VEN, SOM), provider_admin1_name (Nangarhar, Badakhshan, Kunar), provider_admin2_name (Sucre, Libertador, Baydhaba), admin1_code (AF06, AF17, AF15), admin1_name (Nangarhar, Badakhshan, Kunar) and 7 others.

Temporal — reference_period_start, reference_period_end.

Identifier / Metadata — org_name, sector_code, sector_name, esa_source, esa_processed.

Other — has_hrp (Y, N), in_gho (Y).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-hdx-hapi-operational-presence")
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
location_code	object	0.0%	AFG, VEN, SOM
has_hrp	object	0.0%	Y, N
in_gho	object	0.0%	Y
provider_admin1_name	object	0.0%	Nangarhar, Badakhshan, Kunar
provider_admin2_name	object	4.6%	Sucre, Libertador, Baydhaba
admin1_code	object	0.1%	AF06, AF17, AF15
admin1_name	object	0.1%	Nangarhar, Badakhshan, Kunar
admin2_code	object	4.8%	SO2401, VE2313, SO2203
admin2_name	object	4.8%	Sucre, Libertador, Baydhaba
admin_level	int64	0.0%	1.0 – 2.0 (mean 1.9541)
org_acronym	object	0.5%	UNICEF, IOM, UNHCR
org_name	object	0.0%
org_type_description	object	4.0%
sector_code	object	2.0%
sector_name	object	2.0%
reference_period_start	datetime64[ns]	0.0%
reference_period_end	datetime64[ns]	0.0%
dataset_hdx_id	object	0.0%
resource_hdx_id	object	0.0%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
admin_level	1.0	2.0	1.9541	2.0

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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. 2 column(s) with >80% missing values were removed: warning, error. 2 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 HDX Humanitarian API Data and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• This dataset spans 21 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_hdx_hapi_operational_presence,
  title     = {HDX HAPI - Coordination & Context: Operational Presence},
  author    = {HDX Humanitarian API Data},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/hdx-hapi-operational-presence},
  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.