electricsheepafrica/africa-central-african-republic-humanitarian-needs

Central African Republic: Humanitarian Needs

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

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Abstract

This dataset was compiled by the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) on behalf of the Humanitarian Country Team and partners. It provides the Humanitarian Country Team’s shared understanding of the crisis, including the most pressing humanitarian need and the estimated number of people who need assistance, and represents a consolidated evidence base and helps inform joint strategic response planning.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2026-02-13. Geographic scope: CAF.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Tabular records
Rows (total)	15
Columns	6 (2 numeric, 4 categorical, 0 datetime)
Train split	12 rows
Test split	3 rows
Geographic scope	CAF
Publisher	OCHA Humanitarian Programme Cycle Tools (HPC Tools)
HDX last updated	2026-02-13

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Variables

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

Other — description (Final, CCCM, Education), cluster (PRO, ALL, CCM), in_need (range 73000.0–2286959.0), targeted (range 39529.0–1265483.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-central-african-republic-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
description	object	0.0%	Final, CCCM, Education
cluster	object	0.0%	PRO, ALL, CCM
in_need	float64	6.7%	73000.0 – 2286959.0 (mean 832551.0714)
targeted	int64	0.0%	39529.0 – 1265483.0 (mean 411936.8667)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-04

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Numeric Summary

Column	Min	Max	Mean	Median
in_need	73000.0	2286959.0	832551.0714	692738.0
targeted	39529.0	1265483.0	411936.8667	278834.0

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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.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_central_african_republic_humanitarian_needs,
  title     = {Central African Republic: Humanitarian Needs},
  author    = {OCHA Humanitarian Programme Cycle Tools (HPC Tools)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/central-african-republic-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.