electricsheepafrica/africa-global-hpc-hno

Global Humanitarian Programme Cycle, Humanitarian Needs

Publisher: OCHA Humanitarian Programme Cycle Tools (HPC Tools) · Source: HDX · License: cc-by-igo · Updated: 2026-02-13

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Abstract

This dataset contains standardised Humanitarian Needs Overview data taken from the OCHA HPC Tools system which is under active development. For more detailed but less standardized data on humanitarian needs, see the Humanitarian Needs Overview data series.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-02-13. Geographic scope: AFG, BFA, CMR, CAF, TCD, COL, COD, SLV, and 16 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	Country-level aggregates
Rows (total)	134
Columns	7 (2 numeric, 5 categorical, 0 datetime)
Train split	107 rows
Test split	26 rows
Geographic scope	AFG, BFA, CMR, CAF, TCD, COL, COD, SLV, and 16 others
Publisher	OCHA Humanitarian Programme Cycle Tools (HPC Tools)
HDX last updated	2026-02-13

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Variables

Geographic — country_iso3 (COD, CAF, TCD).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-07).

Other — description (Education, Nutrition, GHO Estimates), cluster (ALL, PRO, EDU), in_need (range 73000.0–33699770.0), targeted (range 33445.0–20419163.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-global-hpc-hno")
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
country_iso3	object	0.0%	COD, CAF, TCD
description	object	0.0%	Education, Nutrition, GHO Estimates
cluster	object	0.0%	ALL, PRO, EDU
in_need	float64	4.5%	73000.0 – 33699770.0 (mean 4712172.7266)
targeted	int64	0.0%	33445.0 – 20419163.0 (mean 2097727.9552)
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
in_need	73000.0	33699770.0	4712172.7266	3233415.5
targeted	33445.0	20419163.0	2097727.9552	1236533.0

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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: category, population, affected, reached, info. 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 OCHA Humanitarian Programme Cycle Tools (HPC Tools) 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 24 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_global_hpc_hno,
  title     = {Global Humanitarian Programme Cycle, Humanitarian Needs},
  author    = {OCHA Humanitarian Programme Cycle Tools (HPC Tools)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/global-hpc-hno},
  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.