electricsheepafrica/africa-ethiopia-humanitarian-needs

Ethiopia: Humanitarian Needs

Publisher: OCHA Humanitarian Programme Cycle Tools (HPC Tools) · Source: HDX · License: cc-by · Updated: 2025-12-19

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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 geolocated point observations. Data was last updated on HDX on 2025-12-19. Geographic scope: ETH.

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)	2
Columns	11 (0 numeric, 11 categorical, 0 datetime)
Train split	1 rows
Test split	0 rows
Geographic scope	ETH
Publisher	OCHA Humanitarian Programme Cycle Tools (HPC Tools)
HDX last updated	2025-12-19

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Variables

Geographic — category (#category), population (#population, 129700000).

Outcome / Measurement — affected (#affected).

Identifier / Metadata — esa_source (HDX), esa_processed.

Other — description (#sector+description, GHO Estimate), cluster (#sector+cluster+code, ALL), in_need (#inneed), targeted (#targeted, 10000000), reached (#reached) and 1 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-ethiopia-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%	#sector+description, GHO Estimate
cluster	object	0.0%	#sector+cluster+code, ALL
category	object	50.0%	#category
population	object	0.0%	#population, 129700000
in_need	object	50.0%	#inneed
targeted	object	0.0%	#targeted, 10000000
affected	object	50.0%	#affected
reached	object	50.0%	#reached
info	object	50.0%	#meta+info
esa_source	object	0.0%	HDX
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
No numeric columns.

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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. 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.
• The following columns have >20% missing values and should be treated with caution in modelling: category, in_need, affected, reached, info.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_ethiopia_humanitarian_needs,
  title     = {Ethiopia: Humanitarian Needs},
  author    = {OCHA Humanitarian Programme Cycle Tools (HPC Tools)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/ethiopia-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.