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

Sudan - 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 Sudan, 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: SDN.

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)	17,917
Columns	18 (6 numeric, 11 categorical, 1 datetime)
Train split	14,333 rows
Test split	3,583 rows
Geographic scope	SDN
Publisher	WFP - World Food Programme
HDX last updated	2026-04-05

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Variables

Geographic — admin1 (North Darfur, South Kordofan, Blue Nile), admin2 (Kadugli, El Fasher, Damazin), latitude (range 11.02–19.62), longitude (range 22.45–37.22), category (cereals and tubers, vegetables and fruits, miscellaneous food) and 4 others.

Temporal — date.

Outcome / Measurement — priceflag (actual), price (range 0.5–370833.0), usdprice (range 0.13–617.88).

Identifier / Metadata — market_id (range 1025.0–11377.0), esa_source (HDX), esa_processed.

Other — market (Kadugli, Al Fashir, Damazin), unit (KG, 3 KG, 90 KG).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-wfp-food-prices-for-sudan")
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%	North Darfur, South Kordofan, Blue Nile
admin2	object	0.0%	Kadugli, El Fasher, Damazin
market	object	0.0%	Kadugli, Al Fashir, Damazin
market_id	int64	0.0%	1025.0 – 11377.0 (mean 1585.977)
latitude	float64	0.0%	11.02 – 19.62 (mean 13.7141)
longitude	float64	0.0%	22.45 – 37.22 (mean 30.0111)
category	object	0.0%	cereals and tubers, vegetables and fruits, miscellaneous food
commodity	object	0.0%	Millet, Sorghum, Sorghum (white)
commodity_id	int64	0.0%	58.0 – 1178.0 (mean 225.3348)
unit	object	0.0%	KG, 3 KG, 90 KG
priceflag	object	0.0%	actual
pricetype	object	0.0%	Retail, Wholesale
currency	object	0.0%	SDG
price	float64	0.0%	0.5 – 370833.0 (mean 2899.8243)
usdprice	float64	0.0%	0.13 – 617.88 (mean 13.428)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
market_id	1025.0	11377.0	1585.977	1031.0
latitude	11.02	19.62	13.7141	13.19
longitude	22.45	37.22	30.0111	30.22
commodity_id	58.0	1178.0	225.3348	135.0
price	0.5	370833.0	2899.8243	420.0
usdprice	0.13	617.88	13.428	3.41

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