electricsheepafrica/africa-hdx-hapi-humanitarian-needs

HDX HAPI - Affected People: Humanitarian Needs

Publisher: HDX Humanitarian API Data · Source: HDX · License: cc-by-igo · Updated: 2026-02-13

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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 geolocated point observations. Temporal coverage is indicated by the reference_period_start, reference_period_end column(s). Geographic scope: AFG, BFA, CMR, TCD, COL, COD, SLV, GTM, and 13 others.

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

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Dataset Characteristics

Domain	Demographics and population
Unit of observation	Geolocated point observations
Rows (total)	272
Columns	14 (2 numeric, 10 categorical, 2 datetime)
Train split	217 rows
Test split	54 rows
Geographic scope	AFG, BFA, CMR, TCD, COL, COD, SLV, GTM, and 13 others
Publisher	HDX Humanitarian API Data
HDX last updated	2026-02-13

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Variables

Geographic — location_code (COD, CAF, TCD), admin_level (range 0.0–0.0), population_status (TGT, INN, all), population (range 33445.0–237500000.0), dataset_hdx_id (8326ed53-8f3a-47f9-a2aa-83ab4ecee476) and 1 others.

Temporal — reference_period_start, reference_period_end.

Identifier / Metadata — sector_code (Intersectoral, EDU, FSC), sector_name (Intersectoral, Education, Food Security), esa_source (HDX), esa_processed (2026-04-07).

Other — has_hrp (Y), in_gho (Y).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-hdx-hapi-humanitarian-needs")
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%	COD, CAF, TCD
has_hrp	object	0.0%	Y
in_gho	object	0.0%	Y
admin_level	int64	0.0%	0.0 – 0.0 (mean 0.0)
sector_code	object	0.0%	Intersectoral, EDU, FSC
sector_name	object	0.0%	Intersectoral, Education, Food Security
population_status	object	0.0%	TGT, INN, all
population	int64	0.0%	33445.0 – 237500000.0 (mean 6504748.7868)
reference_period_start	datetime64[ns]	0.0%
reference_period_end	datetime64[ns]	0.0%
dataset_hdx_id	object	0.0%	8326ed53-8f3a-47f9-a2aa-83ab4ecee476
resource_hdx_id	object	0.0%	edb91329-0e6b-4ebc-b6cb-051b2a11e536
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-07

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Numeric Summary

Column	Min	Max	Mean	Median
admin_level	0.0	0.0	0.0	0.0
population	33445.0	237500000.0	6504748.7868	2078939.5

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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. 9 column(s) with >80% missing values were removed: provider_admin1_name, provider_admin2_name, admin1_code, admin1_name, admin2_code, admin2_name.... 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_humanitarian_needs,
  title     = {HDX HAPI - Affected People: Humanitarian Needs},
  author    = {HDX Humanitarian API Data},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/hdx-hapi-humanitarian-needs},
  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.