electricsheepafrica/africa-mauritania-real-time-prices

Mauritania - 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: MRT.

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)	8,085
Columns	87 (74 numeric, 10 categorical, 3 datetime)
Train split	6,468 rows
Test split	1,617 rows
Geographic scope	MRT
Publisher	World Bank Group
HDX last updated	2026-04-01

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Variables

Geographic — iso3 (MRT), country (Mauritania), lat (range 14.77–22.69), lon (range -17.05–-5.52), year (range 2007.0–2026.0) and 20 others.

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

Demographic — data_coverage (range 13.71–13.71), data_coverage_recent (range 21.3–21.3).

Identifier / Metadata — adm1_name (Gorgol, Brakna, Guidimakha), adm2_name (Selibaby, Kaedi, Tintane), mkt_name (Adel Bagrou, Socogim, Nouadhibou), geo_id (gid_155300000-70300000, gid_181000000-159500000, gid_209100000-170500000), esa_source (HDX) and 1 others.

Other — components (couscous_fao (1 Kg, Index Weight = 1), meat_beef_fao (1 Kg, Index Weight = 1), meat_camel_fao (1 Kg, Index Weight = 1), 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 = 1), sugar (1 KG, Index Weight = 1), wheat (1 KG, Index Weight = 1), wheat_flour_fao (1 Kg, Index Weight = 1)), start_dense_data, o_couscous_fao (range 24.11–69.02), h_couscous_fao (range 24.38–70.06), l_couscous_fao (range 23.95–67.81) and 47 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-mauritania-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%	MRT
country	object	0.0%	Mauritania
adm1_name	object	0.0%	Gorgol, Brakna, Guidimakha
adm2_name	object	0.0%	Selibaby, Kaedi, Tintane
mkt_name	object	0.0%	Adel Bagrou, Socogim, Nouadhibou
lat	float64	2.9%	14.77 – 22.69 (mean 17.0641)
lon	float64	2.9%	-17.05 – -5.52 (mean -12.5115)
geo_id	object	0.0%	gid_155300000-70300000, gid_181000000-159500000, gid_209100000-170500000
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%	MRU
components	object	0.0%	couscous_fao (1 Kg, Index Weight = 1), meat_beef_fao (1 Kg, Index Weight = 1), meat_camel_fao (1 Kg, Index Weight = 1), 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 = 1), sugar (1 KG, Index Weight = 1), wheat (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%	13.71 – 13.71 (mean 13.71)
data_coverage_recent	float64	0.0%	21.3 – 21.3 (mean 21.3)
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_couscous_fao	float64	0.0%	24.11 – 69.02 (mean 42.784)
h_couscous_fao	float64	0.0%	24.38 – 70.06 (mean 43.4105)
l_couscous_fao	float64	0.0%	23.95 – 67.81 (mean 42.1663)
c_couscous_fao	float64	0.0%	24.17 – 68.54 (mean 42.8019)
inflation_couscous_fao	float64	5.2%	-13.98 – 53.56 (mean 4.0394)
trust_couscous_fao	float64	0.0%	9.4 – 10.0 (mean 9.6078)
o_meat_beef_fao	float64	0.0%	88.49 – 253.22 (mean 143.8495)
h_meat_beef_fao	float64	0.0%	89.42 – 257.84 (mean 146.2148)
l_meat_beef_fao	float64	0.0%	88.05 – 250.0 (mean 141.5575)
c_meat_beef_fao	float64	0.0%	88.58 – 251.05 (mean 143.8416)
inflation_meat_beef_fao	float64	5.2%	-20.61 – 35.41 (mean 5.1436)
trust_meat_beef_fao	float64	0.0%	9.4 – 10.0 (mean 9.5485)
o_meat_camel_fao	float64	0.0%
h_meat_camel_fao	float64	0.0%
l_meat_camel_fao	float64	0.0%
c_meat_camel_fao	float64	0.0%
inflation_meat_camel_fao	float64	5.2%
trust_meat_camel_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_sugar	float64	0.0%
h_sugar	float64	0.0%
l_sugar	float64	0.0%
c_sugar	float64	0.0%
inflation_sugar	float64	5.2%
trust_sugar	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_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%	2026-04-06

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Numeric Summary

Column	Min	Max	Mean	Median
lat	14.77	22.69	17.0641	16.555
lon	-17.05	-5.52	-12.5115	-12.655
year	2007.0	2026.0	2016.1299	2016.0
month	1.0	12.0	6.4416	6.0
data_coverage	13.71	13.71	13.71	13.71
data_coverage_recent	21.3	21.3	21.3	21.3
index_confidence_score	0.98	0.98	0.98	0.98
spatially_interpolated	0.0	0.0	0.0	0.0
o_couscous_fao	24.11	69.02	42.784	41.87
h_couscous_fao	24.38	70.06	43.4105	42.73
l_couscous_fao	23.95	67.81	42.1663	40.86
c_couscous_fao	24.17	68.54	42.8019	41.92
inflation_couscous_fao	-13.98	53.56	4.0394	0.28
trust_couscous_fao	9.4	10.0	9.6078	9.4
o_meat_beef_fao	88.49	253.22	143.8495	143.05

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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. 773 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_mauritania_real_time_prices,
  title     = {Mauritania - Real Time Prices},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/mauritania-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.