electricsheepafrica/africa-somalia-real-time-prices

Somalia - 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: SOM.

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)	10,626
Columns	110 (97 numeric, 10 categorical, 3 datetime)
Train split	8,500 rows
Test split	2,125 rows
Geographic scope	SOM
Publisher	World Bank Group
HDX last updated	2026-04-01

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Variables

Geographic — iso3 (SOM), country (Somalia), lat (range -0.36–11.26), lon (range 41.0–49.82), year (range 2007.0–2026.0) and 23 others.

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

Demographic — data_coverage (range 15.23–15.23), data_coverage_recent (range 14.65–14.65).

Identifier / Metadata — adm1_name (Gedo, Bakool, Shabelle Hoose), adm2_name (Banadir, Afgooye, Marka), mkt_name (Afgooye, Luuq, Garowe), geo_id (gid_21400000451200000, gid_38000000425400000, gid_84000000484800000), esa_source (HDX) and 1 others.

Other — components (exchange_rate_wfp (1 USD/LCU, Index Weight = 0), fuel_diesel (1 L, Index Weight = 0), livestock_camel_fao (1 unit, Index Weight = 0), livestock_goat_fao (1 unit, Index Weight = 0), maize (1 KG, Index Weight = 0.5), maize_fao (1 Kg, Index Weight = 0.5), milk (1 L, Index Weight = 0.5), milk_fao (1 Liter, Index Weight = 0.5), oil (1 L, Index Weight = 1), rice (1 KG, Index Weight = 0.5), rice_fao (1 Kg, Index Weight = 0.5), sorghum (1 KG, Index Weight = 0.5), sorghum_fao (1 Kg, Index Weight = 0.5), wheat_fao (1 Kg, Index Weight = 1), wheat_flour_fao (1 Kg, Index Weight = 1)), start_dense_data, maize (range 900.0–88000.0), maize_fao (range 900.0–33250.0), rice (range 1500.0–60000.0) and 67 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-somalia-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%	SOM
country	object	0.0%	Somalia
adm1_name	object	0.0%	Gedo, Bakool, Shabelle Hoose
adm2_name	object	0.0%	Banadir, Afgooye, Marka
mkt_name	object	0.0%	Afgooye, Luuq, Garowe
lat	float64	2.2%	-0.36 – 11.26 (mean 5.2178)
lon	float64	2.2%	41.0 – 49.82 (mean 45.3102)
geo_id	object	0.0%	gid_21400000451200000, gid_38000000425400000, gid_84000000484800000
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%	SOS
components	object	0.0%	exchange_rate_wfp (1 USD/LCU, Index Weight = 0), fuel_diesel (1 L, Index Weight = 0), livestock_camel_fao (1 unit, Index Weight = 0), livestock_goat_fao (1 unit, Index Weight = 0), maize (1 KG, Index Weight = 0.5), maize_fao (1 Kg, Index Weight = 0.5), milk (1 L, Index Weight = 0.5), milk_fao (1 Liter, Index Weight = 0.5), oil (1 L, Index Weight = 1), rice (1 KG, Index Weight = 0.5), rice_fao (1 Kg, Index Weight = 0.5), sorghum (1 KG, Index Weight = 0.5), sorghum_fao (1 Kg, Index Weight = 0.5), wheat_fao (1 Kg, Index Weight = 1), wheat_flour_fao (1 Kg, Index Weight = 1)
start_dense_data	datetime64[ns]	0.0%
last_survey_point	datetime64[ns]	0.0%
data_coverage	float64	0.0%	15.23 – 15.23 (mean 15.23)
data_coverage_recent	float64	0.0%	14.65 – 14.65 (mean 14.65)
index_confidence_score	float64	0.0%	0.98 – 0.98 (mean 0.98)
spatially_interpolated	int64	0.0%	0.0 – 0.0 (mean 0.0)
maize	float64	78.2%	900.0 – 88000.0 (mean 16937.3046)
maize_fao	float64	76.2%	900.0 – 33250.0 (mean 9868.1005)
rice	float64	71.7%	1500.0 – 60000.0 (mean 20783.7655)
rice_fao	float64	74.9%	2500.0 – 60000.0 (mean 17226.2596)
sorghum_fao	float64	78.5%	650.0 – 55500.0 (mean 11038.3379)
o_livestock_camel_fao	float64	0.0%	2554633.44 – 26464627.07 (mean 15601536.1961)
h_livestock_camel_fao	float64	0.0%	2790082.85 – 27667379.98 (mean 16185533.1935)
l_livestock_camel_fao	float64	0.0%	2570129.09 – 25375000.0 (mean 15002488.4539)
c_livestock_camel_fao	float64	0.0%	2698565.71 – 26293966.21 (mean 15531873.7804)
inflation_livestock_camel_fao	float64	5.2%	-47.03 – 230.82 (mean 11.0288)
trust_livestock_camel_fao	float64	0.0%	9.0 – 10.0 (mean 9.3527)
o_livestock_goat_fao	float64	0.0%	396063.33 – 3202898.58 (mean 1724073.8856)
h_livestock_goat_fao	float64	0.0%
l_livestock_goat_fao	float64	0.0%
c_livestock_goat_fao	float64	0.0%
inflation_livestock_goat_fao	float64	5.2%
trust_livestock_goat_fao	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_fao	float64	0.0%
h_maize_fao	float64	0.0%
l_maize_fao	float64	0.0%
c_maize_fao	float64	0.0%
inflation_maize_fao	float64	5.2%
trust_maize_fao	float64	0.0%
o_milk	float64	0.0%
h_milk	float64	0.0%
l_milk	float64	0.0%
c_milk	float64	0.0%
inflation_milk	float64	5.2%
trust_milk	float64	0.0%
o_milk_fao	float64	0.0%
h_milk_fao	float64	0.0%
l_milk_fao	float64	0.0%
c_milk_fao	float64	0.0%
inflation_milk_fao	float64	5.2%
trust_milk_fao	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_rice_fao	float64	0.0%
h_rice_fao	float64	0.0%
l_rice_fao	float64	0.0%
c_rice_fao	float64	0.0%
inflation_rice_fao	float64	5.2%
trust_rice_fao	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_sorghum_fao	float64	0.0%
h_sorghum_fao	float64	0.0%
l_sorghum_fao	float64	0.0%
c_sorghum_fao	float64	0.0%
inflation_sorghum_fao	float64	5.2%
trust_sorghum_fao	float64	0.0%
o_wheat_fao	float64	0.0%
h_wheat_fao	float64	0.0%
l_wheat_fao	float64	0.0%
c_wheat_fao	float64	0.0%
inflation_wheat_fao	float64	5.2%
trust_wheat_fao	float64	0.0%
o_wheat_flour_fao	float64	0.0%
h_wheat_flour_fao	float64	0.0%
l_wheat_flour_fao	float64	0.0%
c_wheat_flour_fao	float64	0.0%
inflation_wheat_flour_fao	float64	5.2%
trust_wheat_flour_fao	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	-0.36	11.26	5.2178	4.35
lon	41.0	49.82	45.3102	45.32
year	2007.0	2026.0	2016.1299	2016.0
month	1.0	12.0	6.4416	6.0
data_coverage	15.23	15.23	15.23	15.23
data_coverage_recent	14.65	14.65	14.65	14.65
index_confidence_score	0.98	0.98	0.98	0.98
spatially_interpolated	0.0	0.0	0.0	0.0
maize	900.0	88000.0	16937.3046	13096.77
maize_fao	900.0	33250.0	9868.1005	8750.0
rice	1500.0	60000.0	20783.7655	19745.0
rice_fao	2500.0	60000.0	17226.2596	16000.0
sorghum_fao	650.0	55500.0	11038.3379	8250.0
o_livestock_camel_fao	2554633.44	26464627.07	15601536.1961	15074141.88
h_livestock_camel_fao	2790082.85	27667379.98	16185533.1935	15676321.82

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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. 750 column(s) with >80% missing values were removed: apples, bananas, beans, beans_egyptian, beans_fao, bread.... 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: maize, maize_fao, rice, rice_fao, sorghum_fao.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_somalia_real_time_prices,
  title     = {Somalia - Real Time Prices},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/somalia-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.