electricsheepafrica/africa-sahel-humanitarian-needs-overview

Sahel : Humanitarian Needs Overview

Publisher: OCHA West and Central Africa (ROWCA) · Source: HDX · License: cc-by · Updated: 2025-09-04

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Abstract

This dataset is produced by the United Nations for the Coordination of Humanitarian Affairs (OCHA) in collaboration with humanitarian partners. It covers the period from January to December 2017 and was issued on December 2016.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2025-09-04. Geographic scope: BEN, BFA, CMR, TCD, GMB, MLI, NER, NGA, and 1 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)	28
Columns	8 (1 numeric, 7 categorical, 0 datetime)
Train split	22 rows
Test split	5 rows
Geographic scope	BEN, BFA, CMR, TCD, GMB, MLI, NER, NGA, and 1 others
Publisher	OCHA West and Central Africa (ROWCA)
HDX last updated	2025-09-04

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Variables

Geographic — country (Burkina Faso, Cameroon, Chad).

Outcome / Measurement — affected_2017 (#affected, 119,999, 208,012).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-05).

Other — nutrition (MAM, GAM, PLW), in_need_2017 (#inneed, 92,326, 208,012), targeted_2017 (#targeted, 78,477, 104,006), targeted (range 0.0–100.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-sahel-humanitarian-needs-overview")
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	object	0.0%	Burkina Faso, Cameroon, Chad
nutrition	object	0.0%	MAM, GAM, PLW
affected_2017	object	10.7%	#affected, 119,999, 208,012
in_need_2017	object	10.7%	#inneed, 92,326, 208,012
targeted_2017	object	10.7%	#targeted, 78,477, 104,006
targeted	float64	14.3%	0.0 – 100.0 (mean 64.2083)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-05

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Numeric Summary

Column	Min	Max	Mean	Median
targeted	0.0	100.0	64.2083	70.5

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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. 1 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 OCHA West and Central Africa (ROWCA) 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 9 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_sahel_humanitarian_needs_overview,
  title     = {Sahel : Humanitarian Needs Overview},
  author    = {OCHA West and Central Africa (ROWCA)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/sahel-humanitarian-needs-overview},
  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.