electricsheepafrica/africa-faostat-food-security-indicators-for-mauritania

Mauritania - Food Security and Nutrition Indicators

Publisher: Food and Agriculture Organization (FAO) of the United Nations · Source: HDX · License: cc-by-igo · Updated: 2026-04-06

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Abstract

Food Security and Nutrition Indicators for Mauritania.

Contains data from the FAOSTAT bulk data service.

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

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

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Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	Country-level aggregates
Rows (total)	1,033
Columns	18 (5 numeric, 11 categorical, 2 datetime)
Train split	826 rows
Test split	206 rows
Geographic scope	MRT
Publisher	Food and Agriculture Organization (FAO) of the United Nations
HDX last updated	2026-04-06

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Variables

Geographic — iso3 (MRT), year_code (range 2000.0–20222024.0), year (range 2000.0–2024.0).

Temporal — startdate, enddate.

Outcome / Measurement — value (range -1.17–6397.0).

Identifier / Metadata — area_code (range 136.0–136.0), area_code_m49 ('478), item_code (210071F, 210401M, 210081F), element_code (range 6121.0–61322.0), esa_source (HDX) and 1 others.

Other — area (Mauritania), item (Number of severely food insecure female adults (million) (3-year average), Prevalence of severe food insecurity in the male adult population (percent) (3-year average), Number of moderately or severely food insecure female adults (million) (3-year average)), element (Value, Confidence interval: Lower bound, Confidence interval: Upper bound), unit (%, million No, kcal/cap/d), flag (E, X) and 1 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-faostat-food-security-indicators-for-mauritania")
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
iso3	object	0.0%	MRT
startdate	datetime64[ns]	0.0%
enddate	datetime64[ns]	0.0%
area_code	int64	0.0%	136.0 – 136.0 (mean 136.0)
area_code_m49	object	0.0%	'478
area	object	0.0%	Mauritania
item_code	object	0.0%	210071F, 210401M, 210081F
item	object	0.0%	Number of severely food insecure female adults (million) (3-year average), Prevalence of severe food insecurity in the male adult population (percent) (3-year average), Number of moderately or severely food insecure female adults (million) (3-year average)
element_code	int64	0.0%	6121.0 – 61322.0 (mean 17656.2023)
element	object	0.0%	Value, Confidence interval: Lower bound, Confidence interval: Upper bound
year_code	int64	0.0%	2000.0 – 20222024.0 (mean 10982934.1946)
year	int64	0.0%	2000.0 – 2024.0 (mean 2014.5431)
unit	object	2.1%	%, million No, kcal/cap/d
value	float64	6.6%	-1.17 – 6397.0 (mean 406.3359)
flag	object	0.0%	E, X
note	object	68.3%	Official estimate integrated with FAO data, Age-Adjusted, Imputed value
esa_source	object	0.0%	HDX
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
area_code	136.0	136.0	136.0	136.0
element_code	6121.0	61322.0	17656.2023	6128.0
year_code	2000.0	20222024.0	10982934.1946	20042006.0
year	2000.0	2024.0	2014.5431	2016.0
value	-1.17	6397.0	406.3359	15.0

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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. 3 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 Food and Agriculture Organization (FAO) of the United Nations 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: note.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_faostat_food_security_indicators_for_mauritania,
  title     = {Mauritania - Food Security and Nutrition Indicators},
  author    = {Food and Agriculture Organization (FAO) of the United Nations},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/faostat-food-security-indicators-for-mauritania},
  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.