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

Eritrea - 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 Eritrea, 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: ERI.

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)	100
Columns	18 (6 numeric, 11 categorical, 1 datetime)
Train split	80 rows
Test split	20 rows
Geographic scope	ERI
Publisher	WFP - World Food Programme
HDX last updated	2026-04-05

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Variables

Geographic — admin1 (Maekel, Semenawi Keih Bahri), admin2 (Southern Merab, Massawa), latitude (range 15.33–15.62), longitude (range 38.92–39.44), category (cereals and tubers, pulses and nuts) and 4 others.

Temporal — date.

Outcome / Measurement — priceflag (actual), price (range 900.0–5000.0), usdprice (range 2.0–333.33).

Identifier / Metadata — market_id (range 2564.0–2594.0), esa_source (HDX), esa_processed.

Other — market (Asmara, Massawa), unit (100 KG).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-wfp-food-prices-for-eritrea")
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%	Maekel, Semenawi Keih Bahri
admin2	object	0.0%	Southern Merab, Massawa
market	object	0.0%	Asmara, Massawa
market_id	int64	0.0%	2564.0 – 2594.0 (mean 2569.4)
latitude	float64	0.0%	15.33 – 15.62 (mean 15.3822)
longitude	float64	0.0%	38.92 – 39.44 (mean 39.0136)
category	object	0.0%	cereals and tubers, pulses and nuts
commodity	object	0.0%	Sorghum, Wheat flour, Chickpeas
commodity_id	int64	0.0%	58.0 – 307.0 (mean 137.23)
unit	object	0.0%	100 KG
priceflag	object	0.0%	actual
pricetype	object	0.0%	Retail
currency	object	0.0%	ERN
price	int64	0.0%	900.0 – 5000.0 (mean 2272.0)
usdprice	float64	0.0%	2.0 – 333.33 (mean 126.8071)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
market_id	2564.0	2594.0	2569.4	2564.0
latitude	15.33	15.62	15.3822	15.33
longitude	38.92	39.44	39.0136	38.92
commodity_id	58.0	307.0	137.23	74.5
price	900.0	5000.0	2272.0	2200.0
usdprice	2.0	333.33	126.8071	131.665

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