electricsheepafrica/africa-wfp-food-prices-for-cabo-verde

Cabo Verde - Food Prices

Publisher: WFP - World Food Programme · Source: HDX · License: cc-by-igo · Updated: 2026-04-05

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Abstract

This dataset contains Food Prices data for Cape Verde, sourced from the World Food Programme Price Database. The World Food Programme Price Database covers foods such as maize, rice, beans, fish, and sugar for 98 countries and some 3000 markets. It is updated weekly but contains to a large extent monthly data. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.

Each row in this dataset represents subnational administrative unit observations. Temporal coverage is indicated by the date column(s). Geographic scope: CPV.

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

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Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	Subnational administrative unit observations
Rows (total)	1,884
Columns	18 (6 numeric, 11 categorical, 1 datetime)
Train split	1,507 rows
Test split	376 rows
Geographic scope	CPV
Publisher	WFP - World Food Programme
HDX last updated	2026-04-05

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Variables

Geographic — admin1 (Santo Antao, Sao Vicente, Santiago), admin2 (Concelho de Porto Novo, Sao Vicente, Sao Domingos), latitude (range 14.99–17.02), longitude (range -25.07–-23.52), category (cereals and tubers) and 4 others.

Temporal — date.

Outcome / Measurement — priceflag (actual), price (range 27.41–346.67), usdprice (range 0.34–4.27).

Identifier / Metadata — market_id (range 533.0–535.0), esa_source (HDX), esa_processed.

Other — market (Santo Antao, Sao Vicente, Santiago), unit (KG).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-wfp-food-prices-for-cabo-verde")
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
date	datetime64[ns]	0.0%
admin1	object	0.0%	Santo Antao, Sao Vicente, Santiago
admin2	object	0.0%	Concelho de Porto Novo, Sao Vicente, Sao Domingos
market	object	0.0%	Santo Antao, Sao Vicente, Santiago
market_id	int64	0.0%	533.0 – 535.0 (mean 533.5483)
latitude	float64	0.0%	14.99 – 17.02 (mean 16.8717)
longitude	float64	0.0%	-25.07 – -23.52 (mean -24.9647)
category	object	0.0%	cereals and tubers
commodity	object	0.0%	Wheat flour, Rice (long grain), Cassava
commodity_id	int64	0.0%	56.0 – 162.0 (mean 101.5478)
unit	object	0.0%	KG
priceflag	object	0.0%	actual
pricetype	object	0.0%	Retail
currency	object	0.0%	CVE
price	float64	0.0%	27.41 – 346.67 (mean 88.55)
usdprice	float64	0.0%	0.34 – 4.27 (mean 1.0213)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
market_id	533.0	535.0	533.5483	534.0
latitude	14.99	17.02	16.8717	16.88
longitude	-25.07	-23.52	-24.9647	-24.98
commodity_id	56.0	162.0	101.5478	68.0
price	27.41	346.67	88.55	75.0
usdprice	0.34	4.27	1.0213	0.87

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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. 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.

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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.

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Citation

@dataset{hdx_africa_wfp_food_prices_for_cabo_verde,
  title     = {Cabo Verde - Food Prices},
  author    = {WFP - World Food Programme},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/wfp-food-prices-for-cabo-verde},
  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.