Datasets:

electricsheepafrica
/

africa-wfp-food-prices-for-liberia

annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license: cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - tabular-regression
  - other
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - economics
  - food-security
  - indicators
  - markets
  - lbr
pretty_name: Liberia - Food Prices
dataset_info:
  splits:
    - name: train
      num_examples: 9877
    - name: test
      num_examples: 2469

Liberia - Food Prices

Publisher: WFP - World Food Programme · Source: HDX · License: cc-by-igo · Updated: 2026-04-05

Abstract

This dataset contains Food Prices data for Liberia, 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: LBR.

Curated into ML-ready Parquet format by Electric Sheep Africa.

Dataset Characteristics


Domain	Food security and nutrition
Unit of observation	Subnational administrative unit observations
Rows (total)	12,347
Columns	18 (6 numeric, 11 categorical, 1 datetime)
Train split	9,877 rows
Test split	2,469 rows
Geographic scope	LBR
Publisher	WFP - World Food Programme
HDX last updated	2026-04-05

Variables

Geographic — admin1 (Nimba, Lofa, Maryland), admin2 (Greater Monrovia, Senjeh, Pleebo), latitude (range 4.38–8.42), longitude (range -11.37–-7.63), category (non-food, cereals and tubers, oil and fats) and 4 others.

Temporal — date.

Outcome / Measurement — priceflag (actual, aggregate, actual,aggregate), price (range 5.0–11000.0), usdprice (range 0.06–64.29).

Identifier / Metadata — market_id (range 237.0–3715.0), esa_source (HDX), esa_processed.

Other — market (Pleebo, Red Light, Saclepea), unit (Gallon, 50 KG, KG).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-wfp-food-prices-for-liberia")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column	Type	Null %	Range / Sample Values
`date`	datetime64[ns]	0.0%
`admin1`	object	0.0%	Nimba, Lofa, Maryland
`admin2`	object	0.0%	Greater Monrovia, Senjeh, Pleebo
`market`	object	0.0%	Pleebo, Red Light, Saclepea
`market_id`	int64	0.0%	237.0 – 3715.0 (mean 984.52)
`latitude`	float64	0.0%	4.38 – 8.42 (mean 6.385)
`longitude`	float64	0.0%	-11.37 – -7.63 (mean -9.4398)
`category`	object	0.0%	non-food, cereals and tubers, oil and fats
`commodity`	object	0.0%	Oil (palm), Rice (imported), Fuel (petrol-gasoline)
`commodity_id`	int64	0.0%	62.0 – 713.0 (mean 272.075)
`unit`	object	0.0%	Gallon, 50 KG, KG
`priceflag`	object	0.0%	actual, aggregate, actual,aggregate
`pricetype`	object	0.0%	Retail
`currency`	object	0.0%	LRD
`price`	float64	0.0%	5.0 – 11000.0 (mean 1190.2755)
`usdprice`	float64	0.0%	0.06 – 64.29 (mean 8.9302)
`esa_source`	object	0.0%	HDX
`esa_processed`	object	0.0%

Numeric Summary

Column	Min	Max	Mean	Median
`market_id`	237.0	3715.0	984.52	246.0
`latitude`	4.38	8.42	6.385	6.53
`longitude`	-11.37	-7.63	-9.4398	-9.47
`commodity_id`	62.0	713.0	272.075	285.0
`price`	5.0	11000.0	1190.2755	625.0
`usdprice`	0.06	64.29	8.9302	4.3

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.

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.

Citation

@dataset{hdx_africa_wfp_food_prices_for_liberia,
  title     = {Liberia - Food Prices},
  author    = {WFP - World Food Programme},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/wfp-food-prices-for-liberia},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.