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

Namibia - 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 Namibia, 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: NAM.

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)	5,685
Columns	18 (6 numeric, 11 categorical, 1 datetime)
Train split	4,548 rows
Test split	1,137 rows
Geographic scope	NAM
Publisher	WFP - World Food Programme
HDX last updated	2026-04-05

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Variables

Geographic — admin1 (Oshana, Erongo, Khomas), admin2 (Oshakati East, Swakopmund, Windhoek East), latitude (range -26.58–-17.63), longitude (range 14.53–24.18), category (cereals and tubers) and 4 others.

Temporal — date.

Outcome / Measurement — priceflag (actual), price (range 0.7–66.67), usdprice (range 0.054–4.64).

Identifier / Metadata — market_id (range 2633.0–2640.0), esa_source (HDX), esa_processed.

Other — market (Oshakati, Swakopmund, Windhoek), unit (KG, Unit).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-wfp-food-prices-for-namibia")
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%	Oshana, Erongo, Khomas
admin2	object	0.0%	Oshakati East, Swakopmund, Windhoek East
market	object	0.0%	Oshakati, Swakopmund, Windhoek
market_id	int64	0.0%	2633.0 – 2640.0 (mean 2636.8528)
latitude	float64	0.0%	-26.58 – -17.63 (mean -21.7596)
longitude	float64	0.0%	14.53 – 24.18 (mean 17.5486)
category	object	0.0%	cereals and tubers
commodity	object	0.0%	Maize meal, Wheat flour, Bread
commodity_id	int64	0.0%	52.0 – 571.0 (mean 220.1416)
unit	object	0.0%	KG, Unit
priceflag	object	0.0%	actual
pricetype	object	0.0%	Retail
currency	object	0.0%	NAD
price	float64	0.0%	0.7 – 66.67 (mean 9.9071)
usdprice	float64	0.0%	0.054 – 4.64 (mean 0.8359)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
market_id	2633.0	2640.0	2636.8528	2637.0
latitude	-26.58	-17.63	-21.7596	-22.57
longitude	14.53	24.18	17.5486	17.08
commodity_id	52.0	571.0	220.1416	58.0
price	0.7	66.67	9.9071	9.71
usdprice	0.054	4.64	0.8359	0.85

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