electricsheepafrica/africa-ethiopia-real-time-prices

Ethiopia - 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: ETH.

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)	29,567
Columns	51 (38 numeric, 10 categorical, 3 datetime)
Train split	23,653 rows
Test split	5,913 rows
Geographic scope	ETH
Publisher	World Bank Group
HDX last updated	2026-04-01

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Variables

Geographic — iso3 (ETH), country (Ethiopia), lat (range 3.53–14.45), lon (range 33.95–44.28), year (range 2007.0–2026.0) and 14 others.

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

Demographic — data_coverage (range 21.31–21.31), data_coverage_recent (range 20.26–20.26).

Identifier / Metadata — adm1_name (Oromia, Amhara, Tigray), adm2_name (S. WELLO, E. HARERGE, E. SHEWA), mkt_name (Abaala, Abi Adi, Merti), geo_id (gid_133600000397600000, gid_135600000389700000, gid_84900000398300000), esa_source (HDX) and 1 others.

Other — components (maize (100 KG, Index Weight = 0.01), sorghum (1 KG, Index Weight = 1), teff_fao (100 kg, Index Weight = 0.01), wheat (100 KG, Index Weight = 0.01)), start_dense_data, o_maize (range 78.55–19662.68), h_maize (range 86.31–21571.71), l_maize (range 75.82–17753.65) and 17 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-ethiopia-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%	ETH
country	object	0.0%	Ethiopia
adm1_name	object	0.0%	Oromia, Amhara, Tigray
adm2_name	object	0.0%	S. WELLO, E. HARERGE, E. SHEWA
mkt_name	object	0.0%	Abaala, Abi Adi, Merti
lat	float64	0.8%	3.53 – 14.45 (mean 9.6377)
lon	float64	0.8%	33.95 – 44.28 (mean 39.0413)
geo_id	object	0.0%	gid_133600000397600000, gid_135600000389700000, gid_84900000398300000
dates	datetime64[ns]	0.0%
year	int64	0.0%	2007.0 – 2026.0 (mean 2016.13)
month	int64	0.0%	1.0 – 12.0 (mean 6.4415)
currency	object	0.0%	ETB
components	object	0.0%	maize (100 KG, Index Weight = 0.01), sorghum (1 KG, Index Weight = 1), teff_fao (100 kg, Index Weight = 0.01), wheat (100 KG, Index Weight = 0.01)
start_dense_data	datetime64[ns]	0.0%
last_survey_point	datetime64[ns]	0.0%
data_coverage	float64	0.0%	21.31 – 21.31 (mean 21.31)
data_coverage_recent	float64	0.0%	20.26 – 20.26 (mean 20.26)
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)
o_maize	float64	0.0%	78.55 – 19662.68 (mean 1459.2956)
h_maize	float64	0.0%	86.31 – 21571.71 (mean 1555.1339)
l_maize	float64	0.0%	75.82 – 17753.65 (mean 1364.1976)
c_maize	float64	0.0%	78.87 – 18609.28 (mean 1455.2892)
inflation_maize	float64	5.2%	-76.19 – 436.38 (mean 26.2013)
trust_maize	float64	0.0%	8.8 – 10.0 (mean 9.0272)
o_sorghum	float64	0.0%	0.98 – 231.03 (mean 20.5448)
h_sorghum	float64	0.0%	1.1 – 252.3 (mean 21.708)
l_sorghum	float64	0.0%	0.91 – 169.22 (mean 19.4155)
c_sorghum	float64	0.0%	0.98 – 214.11 (mean 20.5131)
inflation_sorghum	float64	5.2%	-66.41 – 517.99 (mean 25.2844)
trust_sorghum	float64	0.0%	9.8 – 10.0 (mean 9.8431)
o_teff_fao	float64	0.0%
h_teff_fao	float64	0.0%
l_teff_fao	float64	0.0%
c_teff_fao	float64	0.0%
inflation_teff_fao	float64	5.2%
trust_teff_fao	float64	0.0%
o_wheat	float64	0.0%
h_wheat	float64	0.0%
l_wheat	float64	0.0%
c_wheat	float64	0.0%
inflation_wheat	float64	5.2%
trust_wheat	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%	2026-04-06

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Numeric Summary

Column	Min	Max	Mean	Median
lat	3.53	14.45	9.6377	9.48
lon	33.95	44.28	39.0413	39.06
year	2007.0	2026.0	2016.13	2016.0
month	1.0	12.0	6.4415	6.0
data_coverage	21.31	21.31	21.31	21.31
data_coverage_recent	20.26	20.26	20.26	20.26
index_confidence_score	0.98	0.98	0.98	0.98
spatially_interpolated	0.0	0.0	0.0	0.0
o_maize	78.55	19662.68	1459.2956	671.41
h_maize	86.31	21571.71	1555.1339	709.26
l_maize	75.82	17753.65	1364.1976	631.29
c_maize	78.87	18609.28	1455.2892	664.84
inflation_maize	-76.19	436.38	26.2013	15.09
trust_maize	8.8	10.0	9.0272	8.8
o_sorghum	0.98	231.03	20.5448	9.51

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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. 809 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.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

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