electricsheepafrica/africa-fewsnet-staple-food-price-data-for-south-sudan-weekly-6857

South Sudan Weekly FEWS NET Staple Food Price Data

Publisher: FEWS NET · Source: HDX · License: cc-by · Updated: 2026-04-07

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Abstract

South Sudan Weekly staple food price data collected by FEWS NET since 2021.

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the period_date column(s). Geographic scope: SSD.

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

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Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	Country-level aggregates
Rows (total)	48,402
Columns	17 (3 numeric, 13 categorical, 1 datetime)
Train split	38,721 rows
Test split	9,680 rows
Geographic scope	SSD
Publisher	FEWS NET
HDX last updated	2026-04-07

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Variables

Geographic — country (South Sudan), admin_1 (Jonglei, Upper Nile, Central Equatoria), longitude (range 27.3979–33.9249), latitude (range 4.0928–9.8874), price_type (Retail, Wholesale, Wage) and 2 others.

Temporal — period_date.

Outcome / Measurement — value (range 1.0–1120000.0).

Identifier / Metadata — source_document (Famine Early Warning Systems Network (FEWS NET), South Sudan, Price), product_source (Local, Import), esa_source, esa_processed.

Other — market (Leer, Maiwut, Malakal, Aburoc), cpcv2 (R01122AC, R01142AC, R01142AH), product (Maize Grain (White), Sorghum (Red), Sorghum (Feterita)), unit (kg, 3.5_kg, ea).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-fewsnet-staple-food-price-data-for-south-sudan-weekly-6857")
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
country	object	0.0%	South Sudan
market	object	0.0%	Leer, Maiwut, Malakal, Aburoc
admin_1	object	0.0%	Jonglei, Upper Nile, Central Equatoria
longitude	float64	0.0%	27.3979 – 33.9249 (mean 31.3085)
latitude	float64	0.0%	4.0928 – 9.8874 (mean 7.1659)
cpcv2	object	0.0%	R01122AC, R01142AC, R01142AH
product	object	0.0%	Maize Grain (White), Sorghum (Red), Sorghum (Feterita)
source_document	object	0.0%	Famine Early Warning Systems Network (FEWS NET), South Sudan, Price
period_date	datetime64[ns]	0.0%
price_type	object	0.0%	Retail, Wholesale, Wage
product_source	object	0.0%	Local, Import
unit	object	0.0%	kg, 3.5_kg, ea
unit_type	object	0.0%	Weight, Item, Volume
currency	object	0.0%
value	float64	54.2%	1.0 – 1120000.0 (mean 24287.4641)
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
longitude	27.3979	33.9249	31.3085	31.5547
latitude	4.0928	9.8874	7.1659	7.457
value	1.0	1120000.0	24287.4641	3920.0

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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 FEWS NET and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• The following columns have >20% missing values and should be treated with caution in modelling: value.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_fewsnet_staple_food_price_data_for_south_sudan_weekly_6857,
  title     = {South Sudan Weekly FEWS NET Staple Food Price Data},
  author    = {FEWS NET},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/fewsnet_staple_food_price_data_for_south_sudan_weekly_6857},
  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.