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

Tunisia - Food Security and Nutrition Indicators

Publisher: Food and Agriculture Organization (FAO) of the United Nations · Source: HDX · License: cc-by-igo · Updated: 2026-04-13

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Abstract

Food Security and Nutrition Indicators for Tunisia.

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: TUN.

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)	1,115
Columns	18 (5 numeric, 11 categorical, 2 datetime)
Train split	892 rows
Test split	223 rows
Geographic scope	TUN
Publisher	Food and Agriculture Organization (FAO) of the United Nations
HDX last updated	2026-04-13

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Variables

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

Temporal — startdate, enddate.

Outcome / Measurement — value (range -1.14–13249.0).

Identifier / Metadata — area_code (range 222.0–222.0), area_code_m49 ('788), item_code (210071M, 210091F, 210081F), element_code (range 6121.0–61322.0), esa_source (HDX) and 1 others.

Other — area (Tunisia), item (Number of severely food insecure male adults (million) (3-year average), Prevalence of moderate or severe food insecurity in the female 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, Q) and 1 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-faostat-food-security-indicators-for-tunisia")
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%	TUN
startdate	datetime64[ns]	0.0%
enddate	datetime64[ns]	0.0%
area_code	int64	0.0%	222.0 – 222.0 (mean 222.0)
area_code_m49	object	0.0%	'788
area	object	0.0%	Tunisia
item_code	object	0.0%	210071M, 210091F, 210081F
item	object	0.0%	Number of severely food insecure male adults (million) (3-year average), Prevalence of moderate or severe food insecurity in the female 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 16808.0332)
element	object	0.0%	Value, Confidence interval: Lower bound, Confidence interval: Upper bound
year_code	int64	0.0%	2000.0 – 20222024.0 (mean 10229610.5695)
year	int64	0.0%	2000.0 – 2024.0 (mean 2014.157)
unit	object	2.0%	%, million No, kcal/cap/d
value	float64	5.5%	-1.14 – 13249.0 (mean 547.7329)
flag	object	0.0%	E, X, Q
note	object	70.9%	FAO data
esa_source	object	0.0%	HDX
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
area_code	222.0	222.0	222.0	222.0
element_code	6121.0	61322.0	16808.0332	6126.0
year_code	2000.0	20222024.0	10229610.5695	20002002.0
year	2000.0	2024.0	2014.157	2016.0
value	-1.14	13249.0	547.7329	12.75

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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_tunisia,
  title     = {Tunisia - 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-tunisia},
  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.