electricsheepafrica/africa-senegal-real-time-prices

Senegal - 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: SEN.

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)	15,014
Columns	67 (54 numeric, 10 categorical, 3 datetime)
Train split	12,011 rows
Test split	3,002 rows
Geographic scope	SEN
Publisher	World Bank Group
HDX last updated	2026-04-01

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Variables

Geographic — iso3 (SEN), country (Senegal), lat (range 12.48–16.52), lon (range -17.46–-11.95), year (range 2007.0–2026.0) and 16 others.

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

Demographic — data_coverage (range 41.24–41.24), data_coverage_recent (range 27.69–27.69).

Identifier / Metadata — adm1_name (Saint Louis, Fatick, Kaffrine), adm2_name (Dakar, Foundiougne, Matam), mkt_name (Bakel, Bambey, N'Doffane), geo_id (gid_149000000-124600000, gid_147000000-164600000, gid_139200000-159300000), esa_source (HDX) and 1 others.

Other — components (maize (1 KG, Index Weight = 1), millet (1 KG, Index Weight = 0.5), millet_fao (1 Kg, Index Weight = 0.5), rice (1 KG, Index Weight = 1), sorghum (1 KG, Index Weight = 0.5), sorghum_fao (1 Kg, Index Weight = 0.5)), start_dense_data, maize (range 90.0–680.0), millet (range 100.0–750.0), rice (range 150.0–530.0) and 31 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-senegal-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%	SEN
country	object	0.0%	Senegal
adm1_name	object	0.0%	Saint Louis, Fatick, Kaffrine
adm2_name	object	0.0%	Dakar, Foundiougne, Matam
mkt_name	object	0.0%	Bakel, Bambey, N'Doffane
lat	float64	1.5%	12.48 – 16.52 (mean 14.4486)
lon	float64	1.5%	-17.46 – -11.95 (mean -15.5496)
geo_id	object	0.0%	gid_149000000-124600000, gid_147000000-164600000, gid_139200000-159300000
dates	datetime64[ns]	0.0%
year	int64	0.0%	2007.0 – 2026.0 (mean 2016.1301)
month	int64	0.0%	1.0 – 12.0 (mean 6.4412)
currency	object	0.0%	XOF
components	object	0.0%	maize (1 KG, Index Weight = 1), millet (1 KG, Index Weight = 0.5), millet_fao (1 Kg, Index Weight = 0.5), rice (1 KG, Index Weight = 1), sorghum (1 KG, Index Weight = 0.5), sorghum_fao (1 Kg, Index Weight = 0.5)
start_dense_data	datetime64[ns]	0.0%
last_survey_point	datetime64[ns]	0.0%
data_coverage	float64	0.0%	41.24 – 41.24 (mean 41.24)
data_coverage_recent	float64	0.0%	27.69 – 27.69 (mean 27.69)
index_confidence_score	float64	0.0%	0.88 – 0.88 (mean 0.88)
spatially_interpolated	int64	0.0%	0.0 – 0.0 (mean 0.0)
maize	float64	57.5%	90.0 – 680.0 (mean 228.9014)
millet	float64	50.9%	100.0 – 750.0 (mean 243.6519)
rice	float64	51.5%	150.0 – 530.0 (mean 311.0116)
sorghum	float64	69.4%	110.0 – 840.0 (mean 251.0139)
o_maize	float64	0.0%	103.4 – 520.34 (mean 232.4473)
h_maize	float64	0.0%	116.92 – 549.58 (mean 245.8325)
l_maize	float64	0.0%	95.77 – 499.84 (mean 219.3335)
c_maize	float64	0.0%	103.77 – 502.0 (mean 232.9906)
inflation_maize	float64	5.2%	-46.87 – 138.73 (mean 4.1115)
trust_maize	float64	0.0%	5.8 – 10.0 (mean 8.1639)
o_millet	float64	0.0%	106.17 – 738.81 (mean 248.156)
h_millet	float64	0.0%	112.38 – 775.84 (mean 262.7114)
l_millet	float64	0.0%
c_millet	float64	0.0%
inflation_millet	float64	5.2%
trust_millet	float64	0.0%
o_millet_fao	float64	0.0%
h_millet_fao	float64	0.0%
l_millet_fao	float64	0.0%
c_millet_fao	float64	0.0%
inflation_millet_fao	float64	5.2%
trust_millet_fao	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_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_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	12.48	16.52	14.4486	14.57
lon	-17.46	-11.95	-15.5496	-15.96
year	2007.0	2026.0	2016.1301	2016.0
month	1.0	12.0	6.4412	6.0
data_coverage	41.24	41.24	41.24	41.24
data_coverage_recent	27.69	27.69	27.69	27.69
index_confidence_score	0.88	0.88	0.88	0.88
spatially_interpolated	0.0	0.0	0.0	0.0
maize	90.0	680.0	228.9014	213.165
millet	100.0	750.0	243.6519	225.0
rice	150.0	530.0	311.0116	300.0
sorghum	110.0	840.0	251.0139	225.0
o_maize	103.4	520.34	232.4473	218.495
h_maize	116.92	549.58	245.8325	230.585
l_maize	95.77	499.84	219.3335	205.49

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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. 793 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, millet, rice, 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_senegal_real_time_prices,
  title     = {Senegal - Real Time Prices},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/senegal-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.