electricsheepafrica/africa-faostat-food-prices-for-mauritius

Mauritius - Food Prices

Publisher: Food and Agriculture Organization (FAO) of the United Nations · Source: HDX · License: cc-by-igo · Updated: 2026-03-30

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Abstract

Food Prices for Mauritius.

Contains data from the FAOSTAT bulk data service covering the following categories: Consumer Price Indices, Deflators, Exchange rates, Producer Prices

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

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)	438
Columns	17 (6 numeric, 9 categorical, 2 datetime)
Train split	350 rows
Test split	87 rows
Geographic scope	MUS
Publisher	Food and Agriculture Organization (FAO) of the United Nations
HDX last updated	2026-03-30

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Variables

Geographic — iso3 (MUS), year_code (range 1970.0–2024.0), year (range 1970.0–2024.0).

Temporal — startdate, enddate.

Outcome / Measurement — value (range 2.5615–157.1815).

Identifier / Metadata — area_code (range 137.0–137.0), area_code_m49 ('480), item_code (range 22024.0–22028.0), element_code (range 6179.0–62250.0), esa_source (HDX) and 1 others.

Other — area (Mauritius), item (GDP Deflator, Gross Fixed Capital Formation Deflator, Value Added Deflator (Agriculture, forestry and fishery)), element (Value Standard Local Currency, 2015 prices, Value US$, 2015 prices), unit (SLC, USD), flag (X, E).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-faostat-food-prices-for-mauritius")
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%	MUS
startdate	datetime64[ns]	0.0%
enddate	datetime64[ns]	0.0%
area_code	int64	0.0%	137.0 – 137.0 (mean 137.0)
area_code_m49	object	0.0%	'480
area	object	0.0%	Mauritius
item_code	int64	0.0%	22024.0 – 22028.0 (mean 22025.7397)
item	object	0.0%	GDP Deflator, Gross Fixed Capital Formation Deflator, Value Added Deflator (Agriculture, forestry and fishery)
element_code	int64	0.0%	6179.0 – 62250.0 (mean 34214.5)
element	object	0.0%	Value Standard Local Currency, 2015 prices, Value US$, 2015 prices
year_code	int64	0.0%	1970.0 – 2024.0 (mean 1996.8767)
year	int64	0.0%	1970.0 – 2024.0 (mean 1996.8767)
unit	object	0.0%	SLC, USD
value	float64	0.0%	2.5615 – 157.1815 (mean 68.2138)
flag	object	0.0%	X, E
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-06

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Numeric Summary

Column	Min	Max	Mean	Median
area_code	137.0	137.0	137.0	137.0
item_code	22024.0	22028.0	22025.7397	22025.0
element_code	6179.0	62250.0	34214.5	34214.5
year_code	1970.0	2024.0	1996.8767	1997.0
year	1970.0	2024.0	1996.8767	1997.0
value	2.5615	157.1815	68.2138	72.6572

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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. 2 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.
• 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_prices_for_mauritius,
  title     = {Mauritius - Food Prices},
  author    = {Food and Agriculture Organization (FAO) of the United Nations},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/faostat-food-prices-for-mauritius},
  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.