electricsheepafrica/africa-south-sudan-real-time-prices

South Sudan - Real Time Prices

Publisher: World Bank Group · Source: HDX · License: cc-by · Updated: 2026-04-01

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Abstract

Real Time Prices (RTP) is a live dataset compiled and updated weekly by the World Bank Development Economics Data Group (DECDG) using a combination of direct price measurement and Machine Learning estimation of missing price data. The historical and current estimates are based on price information gathered from the World Food Program (WFP), UN-Food and Agricultural Organization (FAO), select National Statistical Offices, and are continually updated and revised as more price information becomes available. Real-time exchange rate data used in this process are from official and public sources.

RTP includes three sub-series, Real Time Food Prices (RTFP) includes prices on a variety of food items that primarily include country-specific staple foods, Real Time Energy Prices (RTEP) includes fuel prices, and Real Time Exchange Rates (RTFX) and includes unofficial exchange rate estimates as well as possible other unofficial deflators.

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the dates, start_dense_data 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)	9,009
Columns	107 (94 numeric, 10 categorical, 3 datetime)
Train split	7,207 rows
Test split	1,801 rows
Geographic scope	SSD
Publisher	World Bank Group
HDX last updated	2026-04-01

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Variables

Geographic — iso3 (SSD), country (South Sudan), lat (range 4.24–11.74), lon (range 26.87–33.73), year (range 2007.0–2026.0) and 23 others.

Temporal — dates, month (range 1.0–12.0).

Demographic — data_coverage (range 27.12–27.12), data_coverage_recent (range 42.44–42.44), o_livestockgoat_male, h_livestockgoat_male, l_livestockgoat_male and 7 others.

Identifier / Metadata — adm1_name (Unity, Jonglei, Warrap), adm2_name (Pariang, Twic, Juba), mkt_name (Abyei, Suk Shabi, Old Fangak), geo_id (gid_93500000282800000, gid_117400000328100000, gid_90700000308800000), esa_source (HDX) and 1 others.

Other — components (beans (1 KG, Index Weight = 1), fuel_diesel (1 L, Index Weight = 0), fuel_petrol_gasoline (1 L, Index Weight = 0), fuel_petrol_gasoline_parallel_market (1 L, Index Weight = 0), groundnuts (1 KG, Index Weight = 1), livestockgoat_male (1 Head, Index Weight = 0), livestocksheep_male (1 Head, Index Weight = 0), maize (3.5 KG, Index Weight = 0.29), maize_meal (1 KG, Index Weight = 1), millet (3.5 KG, Index Weight = 0.29), milling_cost_sorghum (LCU/3.5kg, Index Weight = 0), oil (1 L, Index Weight = 1), rice (1 KG, Index Weight = 1), salt (1 KG, Index Weight = 1), sesame (3.5 KG, Index Weight = 0.29), sorghum (3.5 KG, Index Weight = 0.29), wage_non_qualified_labour_non_agricultural (1 Day, Index Weight = 0), wheat_flour (1 KG, Index Weight = 1)), start_dense_data, beans (range -8.79–71100.0), sorghum (range -54.39–70000.0), o_beans (range 3.92–47630.83) and 54 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-south-sudan-real-time-prices")
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
iso3	object	0.0%	SSD
country	object	0.0%	South Sudan
adm1_name	object	0.0%	Unity, Jonglei, Warrap
adm2_name	object	0.0%	Pariang, Twic, Juba
mkt_name	object	0.0%	Abyei, Suk Shabi, Old Fangak
lat	float64	2.6%	4.24 – 11.74 (mean 8.0134)
lon	float64	2.6%	26.87 – 33.73 (mean 30.2797)
geo_id	object	0.0%	gid_93500000282800000, gid_117400000328100000, gid_90700000308800000
dates	datetime64[ns]	0.0%
year	int64	0.0%	2007.0 – 2026.0 (mean 2016.1299)
month	int64	0.0%	1.0 – 12.0 (mean 6.4416)
currency	object	0.0%	SSP
components	object	0.0%	beans (1 KG, Index Weight = 1), fuel_diesel (1 L, Index Weight = 0), fuel_petrol_gasoline (1 L, Index Weight = 0), fuel_petrol_gasoline_parallel_market (1 L, Index Weight = 0), groundnuts (1 KG, Index Weight = 1), livestockgoat_male (1 Head, Index Weight = 0), livestocksheep_male (1 Head, Index Weight = 0), maize (3.5 KG, Index Weight = 0.29), maize_meal (1 KG, Index Weight = 1), millet (3.5 KG, Index Weight = 0.29), milling_cost_sorghum (LCU/3.5kg, Index Weight = 0), oil (1 L, Index Weight = 1), rice (1 KG, Index Weight = 1), salt (1 KG, Index Weight = 1), sesame (3.5 KG, Index Weight = 0.29), sorghum (3.5 KG, Index Weight = 0.29), wage_non_qualified_labour_non_agricultural (1 Day, Index Weight = 0), wheat_flour (1 KG, Index Weight = 1)
start_dense_data	datetime64[ns]	0.0%
last_survey_point	datetime64[ns]	0.0%
data_coverage	float64	0.0%	27.12 – 27.12 (mean 27.12)
data_coverage_recent	float64	0.0%	42.44 – 42.44 (mean 42.44)
index_confidence_score	float64	0.0%	0.96 – 0.96 (mean 0.96)
spatially_interpolated	int64	0.0%	0.0 – 0.0 (mean 0.0)
beans	float64	77.7%	-8.79 – 71100.0 (mean 2657.351)
sorghum	float64	77.2%	-54.39 – 70000.0 (mean 3162.6668)
o_beans	float64	0.0%	3.92 – 47630.83 (mean 1593.7026)
h_beans	float64	0.0%	4.22 – 52881.05 (mean 1771.2352)
l_beans	float64	0.0%	3.57 – 25925.05 (mean 1416.8487)
c_beans	float64	0.0%	4.0 – 41722.04 (mean 1565.069)
inflation_beans	float64	5.2%	-68.92 – 1236.8 (mean 74.9386)
trust_beans	float64	0.0%	8.9 – 10.0 (mean 9.1566)
o_groundnuts	float64	0.0%	2.8 – 21351.28 (mean 1167.0635)
h_groundnuts	float64	0.0%	3.07 – 22507.76 (mean 1275.8031)
l_groundnuts	float64	0.0%	2.69 – 19503.89 (mean 1062.9093)
c_groundnuts	float64	0.0%	2.88 – 20275.1 (mean 1158.7543)
inflation_groundnuts	float64	5.2%
trust_groundnuts	float64	0.0%
o_livestockgoat_male	float64	0.0%
h_livestockgoat_male	float64	0.0%
l_livestockgoat_male	float64	0.0%
c_livestockgoat_male	float64	0.0%
inflation_livestockgoat_male	float64	5.2%
trust_livestockgoat_male	float64	0.0%
o_livestocksheep_male	float64	0.0%
h_livestocksheep_male	float64	0.0%
l_livestocksheep_male	float64	0.0%
c_livestocksheep_male	float64	0.0%
inflation_livestocksheep_male	float64	5.2%
trust_livestocksheep_male	float64	0.0%
o_maize	float64	0.0%
h_maize	float64	0.0%
l_maize	float64	0.0%
c_maize	float64	0.0%
inflation_maize	float64	5.2%
trust_maize	float64	0.0%
o_maize_meal	float64	0.0%
h_maize_meal	float64	0.0%
l_maize_meal	float64	0.0%
c_maize_meal	float64	0.0%
inflation_maize_meal	float64	5.2%
trust_maize_meal	float64	0.0%
o_millet	float64	0.0%
h_millet	float64	0.0%
l_millet	float64	0.0%
c_millet	float64	0.0%
inflation_millet	float64	5.2%
trust_millet	float64	0.0%
o_oil	float64	0.0%
h_oil	float64	0.0%
l_oil	float64	0.0%
c_oil	float64	0.0%
inflation_oil	float64	5.2%
trust_oil	float64	0.0%
o_rice	float64	0.0%
h_rice	float64	0.0%
l_rice	float64	0.0%
c_rice	float64	0.0%
inflation_rice	float64	5.2%
trust_rice	float64	0.0%
o_salt	float64	0.0%
h_salt	float64	0.0%
l_salt	float64	0.0%
c_salt	float64	0.0%
inflation_salt	float64	5.2%
trust_salt	float64	0.0%
o_sesame	float64	0.0%
h_sesame	float64	0.0%
l_sesame	float64	0.0%
c_sesame	float64	0.0%
inflation_sesame	float64	5.2%
trust_sesame	float64	0.0%
o_sorghum	float64	0.0%
h_sorghum	float64	0.0%
l_sorghum	float64	0.0%
c_sorghum	float64	0.0%
inflation_sorghum	float64	5.2%
trust_sorghum	float64	0.0%
o_wheat_flour	float64	0.0%
h_wheat_flour	float64	0.0%
l_wheat_flour	float64	0.0%
c_wheat_flour	float64	0.0%
inflation_wheat_flour	float64	5.2%
trust_wheat_flour	float64	0.0%
o_food_price_index	float64	0.0%
h_food_price_index	float64	0.0%
l_food_price_index	float64	0.0%
c_food_price_index	float64	0.0%
inflation_food_price_index	float64	5.2%
trust_food_price_index	float64	0.0%
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-08

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Numeric Summary

Column	Min	Max	Mean	Median
lat	4.24	11.74	8.0134	8.67
lon	26.87	33.73	30.2797	30.125
year	2007.0	2026.0	2016.1299	2016.0
month	1.0	12.0	6.4416	6.0
data_coverage	27.12	27.12	27.12	27.12
data_coverage_recent	42.44	42.44	42.44	42.44
index_confidence_score	0.96	0.96	0.96	0.96
spatially_interpolated	0.0	0.0	0.0	0.0
beans	-8.79	71100.0	2657.351	675.0
sorghum	-54.39	70000.0	3162.6668	638.0
o_beans	3.92	47630.83	1593.7026	67.74
h_beans	4.22	52881.05	1771.2352	78.55
l_beans	3.57	25925.05	1416.8487	60.0
c_beans	4.0	41722.04	1565.069	75.64
inflation_beans	-68.92	1236.8	74.9386	36.19

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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. 753 column(s) with >80% missing values were removed: apples, bananas, beans_egyptian, beans_fao, bread, bread_fao.... 3 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 World Bank Group 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: beans, sorghum.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_south_sudan_real_time_prices,
  title     = {South Sudan - Real Time Prices},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/south-sudan-real-time-prices},
  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.