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

Eswatini - Food Prices

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 Prices for Eswatini.

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

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

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)	906
Columns	20 (7 numeric, 11 categorical, 2 datetime)
Train split	724 rows
Test split	181 rows
Geographic scope	SWZ
Publisher	Food and Agriculture Organization (FAO) of the United Nations
HDX last updated	2026-04-06

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Variables

Geographic — iso3 (SWZ), year_code (range 2000.0–2025.0), year (range 2000.0–2025.0).

Temporal — startdate, enddate, months_code (range 7001.0–7012.0), months (January, February, March).

Outcome / Measurement — value (range -8.4728–180.6673).

Identifier / Metadata — area_code (range 209.0–209.0), area_code_m49 ('748), item_code (range 23012.0–23014.0), element_code (range 6121.0–6125.0), esa_source (HDX) and 1 others.

Other — area (Eswatini), item (Consumer Prices, Food Indices (2015 = 100), Consumer Prices, General Indices (2015 = 100), Food price inflation), element (Value), unit (%), flag (X, E, I) and 1 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-faostat-food-prices-for-eswatini")
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%	SWZ
startdate	datetime64[ns]	0.0%
enddate	datetime64[ns]	0.0%
area_code	int64	0.0%	209.0 – 209.0 (mean 209.0)
area_code_m49	object	0.0%	'748
area	object	0.0%	Eswatini
item_code	int64	0.0%	23012.0 – 23014.0 (mean 23012.9868)
item	object	0.0%	Consumer Prices, Food Indices (2015 = 100), Consumer Prices, General Indices (2015 = 100), Food price inflation
element_code	int64	0.0%	6121.0 – 6125.0 (mean 6123.702)
element	object	0.0%	Value
months_code	int64	0.0%	7001.0 – 7012.0 (mean 7006.4404)
months	object	0.0%	January, February, March
year_code	int64	0.0%	2000.0 – 2025.0 (mean 2012.4172)
year	int64	0.0%	2000.0 – 2025.0 (mean 2012.4172)
unit	object	67.5%	%
value	float64	0.0%	-8.4728 – 180.6673 (mean 63.5677)
flag	object	0.0%	X, E, I
note	object	32.5%	base year is 2015
esa_source	object	0.0%	HDX
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
area_code	209.0	209.0	209.0	209.0
item_code	23012.0	23014.0	23012.9868	23013.0
element_code	6121.0	6125.0	6123.702	6125.0
months_code	7001.0	7012.0	7006.4404	7006.0
year_code	2000.0	2025.0	2012.4172	2012.0
year	2000.0	2025.0	2012.4172	2012.0
value	-8.4728	180.6673	63.5677	52.4246

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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.
• The following columns have >20% missing values and should be treated with caution in modelling: unit, 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_prices_for_eswatini,
  title     = {Eswatini - Food Prices},
  author    = {Food and Agriculture Organization (FAO) of the United Nations},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/faostat-food-prices-for-eswatini},
  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.