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

Burundi - 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 Burundi, 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: BDI.

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)	89,907
Columns	18 (6 numeric, 11 categorical, 1 datetime)
Train split	71,925 rows
Test split	17,981 rows
Geographic scope	BDI
Publisher	WFP - World Food Programme
HDX last updated	2026-04-05

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Variables

Geographic — admin1 (Muyinga, Kirundo, Ruyigi), admin2 (Itaba, Ntahangwa, Rugombo), latitude (range -4.31–-2.44), longitude (range 29.08–30.78), category (cereals and tubers, vegetables and fruits, meat, fish and eggs) and 4 others.

Temporal — date.

Outcome / Measurement — priceflag (aggregate, actual), price (range 30.0–145196.08), usdprice (range 0.025–49.37).

Identifier / Metadata — market_id (range 201.0–3055.0), esa_source (HDX), esa_processed.

Other — market (Gitega, Kirundo, Ngozi), unit (KG, L, USD/LCU).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-wfp-food-prices-for-burundi")
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%	Muyinga, Kirundo, Ruyigi
admin2	object	0.0%	Itaba, Ntahangwa, Rugombo
market	object	0.0%	Gitega, Kirundo, Ngozi
market_id	int64	0.0%	201.0 – 3055.0 (mean 2198.5146)
latitude	float64	0.0%	-4.31 – -2.44 (mean -3.2774)
longitude	float64	0.0%	29.08 – 30.78 (mean 29.8807)
category	object	0.0%	cereals and tubers, vegetables and fruits, meat, fish and eggs
commodity	object	0.0%	Sweet potatoes, Cassava flour, Onions
commodity_id	int64	0.0%	50.0 – 829.0 (mean 216.3941)
unit	object	0.0%	KG, L, USD/LCU
priceflag	object	0.0%	aggregate, actual
pricetype	object	0.0%	Retail, Wholesale
currency	object	0.0%	BIF
price	float64	0.0%	30.0 – 145196.08 (mean 4448.9608)
usdprice	float64	0.0%	0.025 – 49.37 (mean 1.9025)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
market_id	201.0	3055.0	2198.5146	2449.0
latitude	-4.31	-2.44	-3.2774	-3.26
longitude	29.08	30.78	29.8807	29.83
commodity_id	50.0	829.0	216.3941	148.0
price	30.0	145196.08	4448.9608	1800.0
usdprice	0.025	49.37	1.9025	0.82

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