Datasets:

electricsheepafrica
/

africa-wfp-hungermap-data-for-mrt

HungerMapLIVE is the World Food Programme (WFP)’s global hunger monitoring system. It combines key metrics from various data sources – such as food security information, weather, population size, conflict, hazards, nutrition information and macro-economic data – to help assess, monitor and predict the magnitude and severity of hunger in near real-time. The resulting analysis is displayed on an interactive map that helps WFP staff, key decision makers and the broader humanitarian community to make more informed and timely decisions relating to food security.

The platform covers 94 countries, including countries where WFP has operations as well as most lower and lower-middle income countries (as classified by the World Bank).

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the date column(s). Geographic scope: MRT.

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

Dataset Characteristics


Domain	Food security and nutrition
Unit of observation	Country-level aggregates
Rows (total)	1
Columns	15 (8 numeric, 6 categorical, 1 datetime)
Train split	0 rows
Test split	0 rows
Geographic scope	MRT
Publisher	WFP - World Food Programme
HDX last updated	2026-03-04

Variables

Geographic — countrycode (MRT), countryname (Mauritania), adminlevel (national), datatype (SURVEY).

Temporal — date.

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-06).

Other — fcs_people (range 1499498.0–1499498.0), fcs_prevalence (range 0.3405–0.3405), rcsi_people (range 132765.0–132765.0), rcsi_prevalence (range 0.0302–0.0302), health_access_people (range 1180164.0–1180164.0) and 3 others.

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-wfp-hungermap-data-for-mrt")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column	Type	Null %	Range / Sample Values
`countrycode`	object	0.0%	MRT
`countryname`	object	0.0%	Mauritania
`adminlevel`	object	0.0%	national
`date`	datetime64[ns]	0.0%
`datatype`	object	0.0%	SURVEY
`fcs_people`	int64	0.0%	1499498.0 – 1499498.0 (mean 1499498.0)
`fcs_prevalence`	float64	0.0%	0.3405 – 0.3405 (mean 0.3405)
`rcsi_people`	int64	0.0%	132765.0 – 132765.0 (mean 132765.0)
`rcsi_prevalence`	float64	0.0%	0.0302 – 0.0302 (mean 0.0302)
`health_access_people`	int64	0.0%	1180164.0 – 1180164.0 (mean 1180164.0)
`health_access_prevalence`	float64	0.0%	0.6697 – 0.6697 (mean 0.6697)
`market_access_people`	int64	0.0%	2296737.0 – 2296737.0 (mean 2296737.0)
`market_access_prevalence`	float64	0.0%	0.5216 – 0.5216 (mean 0.5216)
`esa_source`	object	0.0%	HDX
`esa_processed`	object	0.0%	2026-04-06

Numeric Summary

Column	Min	Max	Mean	Median
`fcs_people`	1499498.0	1499498.0	1499498.0	1499498.0
`fcs_prevalence`	0.3405	0.3405	0.3405	0.3405
`rcsi_people`	132765.0	132765.0	132765.0	132765.0
`rcsi_prevalence`	0.0302	0.0302	0.0302	0.0302
`health_access_people`	1180164.0	1180164.0	1180164.0	1180164.0
`health_access_prevalence`	0.6697	0.6697	0.6697	0.6697
`market_access_people`	2296737.0	2296737.0	2296737.0	2296737.0
`market_access_prevalence`	0.5216	0.5216	0.5216	0.5216

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. 2 column(s) with >80% missing values were removed: adminone, population. 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.

Limitations

Data originates from WFP - World Food Programme 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.

Citation

@dataset{hdx_africa_wfp_hungermap_data_for_mrt,
  title     = {Mauritania - HungerMap data},
  author    = {WFP - World Food Programme},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/wfp-hungermap-data-for-mrt},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.

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Collection including electricsheepafrica/africa-wfp-hungermap-data-for-mrt

Africa · Food Security & Agriculture

Collection

Food security, agriculture, and nutrition data for Africa. Sources: FAOSTAT, FEWS NET, WFP food prices, HungerMap. • 345 items • Updated 7 days ago