electricsheepafrica/africa-burkina-faso-real-time-prices

Burkina Faso - 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: BFA.

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)	15,015
Columns	79 (66 numeric, 10 categorical, 3 datetime)
Train split	12,012 rows
Test split	3,003 rows
Geographic scope	BFA
Publisher	World Bank Group
HDX last updated	2026-04-01

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Variables

Geographic — iso3 (BFA), country (Burkina Faso), lat (range 9.88–14.64), lon (range -5.11–1.82), year (range 2007.0–2026.0) and 18 others.

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

Demographic — data_coverage (range 32.61–32.61), data_coverage_recent (range 42.76–42.76).

Identifier / Metadata — adm1_name (BOUCLE DU MOUHOUN, SAHEL, EST), adm2_name (SOUM, HOUET, BOULGOU), mkt_name (Arbinda, Koudougou, Leo), geo_id (gid_142300000-8600000, gid_122500000-23700000, gid_111000000-21000000), esa_source (HDX) and 1 others.

Other — components (beans (1 KG, Index Weight = 2), maize (1 KG, Index Weight = 1), maize_fao (100 kg, Index Weight = 0.01), millet (1 KG, Index Weight = 1), millet_fao (100 kg, Index Weight = 0.01), rice_fao (100 kg, Index Weight = 0.02), sorghum (1 KG, Index Weight = 1), sorghum_fao (100 kg, Index Weight = 0.01)), start_dense_data, beans (range 82.0–1833.0), maize (range 65.0–1336.0), millet (range 70.33–908.0) and 41 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-burkina-faso-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%	BFA
country	object	0.0%	Burkina Faso
adm1_name	object	0.0%	BOUCLE DU MOUHOUN, SAHEL, EST
adm2_name	object	0.0%	SOUM, HOUET, BOULGOU
mkt_name	object	0.0%	Arbinda, Koudougou, Leo
lat	float64	1.5%	9.88 – 14.64 (mean 12.2653)
lon	float64	1.5%	-5.11 – 1.82 (mean -1.7914)
geo_id	object	0.0%	gid_142300000-8600000, gid_122500000-23700000, gid_111000000-21000000
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%	XOF
components	object	0.0%	beans (1 KG, Index Weight = 2), maize (1 KG, Index Weight = 1), maize_fao (100 kg, Index Weight = 0.01), millet (1 KG, Index Weight = 1), millet_fao (100 kg, Index Weight = 0.01), rice_fao (100 kg, Index Weight = 0.02), sorghum (1 KG, Index Weight = 1), sorghum_fao (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%	32.61 – 32.61 (mean 32.61)
data_coverage_recent	float64	0.0%	42.76 – 42.76 (mean 42.76)
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)
beans	float64	63.0%	82.0 – 1833.0 (mean 396.6015)
maize	float64	64.8%	65.0 – 1336.0 (mean 183.5554)
millet	float64	58.0%	70.33 – 908.0 (mean 246.0785)
sorghum	float64	60.7%	60.5 – 1323.0 (mean 202.9255)
o_beans	float64	0.0%	80.61 – 1433.97 (mean 344.6639)
h_beans	float64	0.0%	95.35 – 1518.71 (mean 366.5362)
l_beans	float64	0.0%	65.86 – 1321.17 (mean 322.8)
c_beans	float64	0.0%	84.82 – 1334.93 (mean 344.7424)
inflation_beans	float64	5.2%	-74.72 – 243.59 (mean 9.5407)
trust_beans	float64	0.0%	8.2 – 10.0 (mean 9.0145)
o_maize	float64	0.0%	60.95 – 514.85 (mean 173.7664)
h_maize	float64	0.0%	64.72 – 540.61 (mean 182.592)
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_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_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_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_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	9.88	14.64	12.2653	12.235
lon	-5.11	1.82	-1.7914	-1.7
year	2007.0	2026.0	2016.1299	2016.0
month	1.0	12.0	6.4416	6.0
data_coverage	32.61	32.61	32.61	32.61
data_coverage_recent	42.76	42.76	42.76	42.76
index_confidence_score	0.98	0.98	0.98	0.98
spatially_interpolated	0.0	0.0	0.0	0.0
beans	82.0	1833.0	396.6015	356.0
maize	65.0	1336.0	183.5554	170.0
millet	70.33	908.0	246.0785	224.0
sorghum	60.5	1323.0	202.9255	183.0
o_beans	80.61	1433.97	344.6639	309.06
h_beans	95.35	1518.71	366.5362	328.23
l_beans	65.86	1321.17	322.8	290.0

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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. 781 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, maize, millet, 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_burkina_faso_real_time_prices,
  title     = {Burkina Faso - Real Time Prices},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/burkina-faso-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.