electricsheepafrica/africa-wfp-food-prices-for-guinea

Guinea - 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 Guinea, 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: GIN.

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)	18,758
Columns	18 (6 numeric, 11 categorical, 1 datetime)
Train split	15,006 rows
Test split	3,751 rows
Geographic scope	GIN
Publisher	WFP - World Food Programme
HDX last updated	2026-04-05

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Variables

Geographic — admin1 (Nzerekore, Boke, Faranah), admin2 (Conakry, Nzerekore, Labe), latitude (range 7.75–12.48), longitude (range -14.3–-8.53), category (cereals and tubers, meat, fish and eggs, miscellaneous food) and 4 others.

Temporal — date.

Outcome / Measurement — priceflag (actual), price (range 500.0–93333.0), usdprice (range 0.071–10.98).

Identifier / Metadata — market_id (range 354.0–2572.0), esa_source (HDX), esa_processed.

Other — market (Grand marché (Nzerekore), Marché central (Labé), Dibida (Kankan)), unit (KG, L, Unit).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-wfp-food-prices-for-guinea")
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%	Nzerekore, Boke, Faranah
admin2	object	0.0%	Conakry, Nzerekore, Labe
market	object	0.0%	Grand marché (Nzerekore), Marché central (Labé), Dibida (Kankan)
market_id	int64	0.0%	354.0 – 2572.0 (mean 1519.3505)
latitude	float64	0.0%	7.75 – 12.48 (mean 9.5962)
longitude	float64	0.0%	-14.3 – -8.53 (mean -10.8424)
category	object	0.0%	cereals and tubers, meat, fish and eggs, miscellaneous food
commodity	object	0.0%	Rice (local), Rice (imported), Beans (niebe, white)
commodity_id	int64	0.0%	51.0 – 503.0 (mean 173.6177)
unit	object	0.0%	KG, L, Unit
priceflag	object	0.0%	actual
pricetype	object	0.0%	Retail
currency	object	0.0%	GNF
price	float64	0.0%	500.0 – 93333.0 (mean 13022.9979)
usdprice	float64	0.0%	0.071 – 10.98 (mean 1.4826)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
market_id	354.0	2572.0	1519.3505	1639.0
latitude	7.75	12.48	9.5962	9.51
longitude	-14.3	-8.53	-10.8424	-10.11
commodity_id	51.0	503.0	173.6177	114.0
price	500.0	93333.0	13022.9979	10000.0
usdprice	0.071	10.98	1.4826	1.15

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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_guinea,
  title     = {Guinea - Food Prices},
  author    = {WFP - World Food Programme},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/wfp-food-prices-for-guinea},
  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.