electricsheepafrica/africa-burundi-real-time-prices

Burundi - 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: BDI.

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)	17,325
Columns	97 (84 numeric, 10 categorical, 3 datetime)
Train split	13,860 rows
Test split	3,465 rows
Geographic scope	BDI
Publisher	World Bank Group
HDX last updated	2026-04-01

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Variables

Geographic — iso3 (BDI), country (Burundi), lat (range -4.31–-2.44), lon (range 29.08–30.78), year (range 2007.0–2026.0) and 20 others.

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

Demographic — data_coverage (range 46.51–46.51), data_coverage_recent (range 82.16–82.16).

Identifier / Metadata — adm1_name (Ruyigi, Muyinga, Kirundo), adm2_name (Ntahangwa, Butaganzwa1, Giharo), mkt_name (Bandaga, Muyinga, Mutaho), geo_id (gid_-30700000296100000, gid_-28500000303400000, gid_-31400000298700000), esa_source (HDX) and 1 others.

Other — components (bananas (1 KG, Index Weight = 1), beans (1 KG, Index Weight = 1), cassava_flour (1 KG, Index Weight = 1), maize (1 KG, Index Weight = 1), maize_flour (1 KG, Index Weight = 1), meat_goat (1 KG, Index Weight = 1), onions (1 KG, Index Weight = 1), potatoes (1 KG, Index Weight = 1), rice (1 KG, Index Weight = 1), tomatoes (1 KG, Index Weight = 1)), start_dense_data, bananas (range 141.67–5000.0), beans (range 400.0–4396.67), cassava_flour (range 250.0–2701.59) and 57 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-burundi-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%	BDI
country	object	0.0%	Burundi
adm1_name	object	0.0%	Ruyigi, Muyinga, Kirundo
adm2_name	object	0.0%	Ntahangwa, Butaganzwa1, Giharo
mkt_name	object	0.0%	Bandaga, Muyinga, Mutaho
lat	float64	1.3%	-4.31 – -2.44 (mean -3.2872)
lon	float64	1.3%	29.08 – 30.78 (mean 29.8661)
geo_id	object	0.0%	gid_-30700000296100000, gid_-28500000303400000, gid_-31400000298700000
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%	BIF
components	object	0.0%	bananas (1 KG, Index Weight = 1), beans (1 KG, Index Weight = 1), cassava_flour (1 KG, Index Weight = 1), maize (1 KG, Index Weight = 1), maize_flour (1 KG, Index Weight = 1), meat_goat (1 KG, Index Weight = 1), onions (1 KG, Index Weight = 1), potatoes (1 KG, Index Weight = 1), rice (1 KG, Index Weight = 1), tomatoes (1 KG, Index Weight = 1)
start_dense_data	datetime64[ns]	0.0%
last_survey_point	datetime64[ns]	0.0%
data_coverage	float64	0.0%	46.51 – 46.51 (mean 46.51)
data_coverage_recent	float64	0.0%	82.16 – 82.16 (mean 82.16)
index_confidence_score	float64	0.0%	0.91 – 0.91 (mean 0.91)
spatially_interpolated	int64	0.0%	0.0 – 0.0 (mean 0.0)
bananas	float64	73.1%	141.67 – 5000.0 (mean 974.7673)
beans	float64	70.2%	400.0 – 4396.67 (mean 1662.5941)
cassava_flour	float64	67.4%	250.0 – 2701.59 (mean 1106.2669)
maize	float64	71.1%	226.0 – 3081.82 (mean 1176.9644)
maize_flour	float64	73.6%	460.0 – 4866.67 (mean 1491.7361)
meat_goat	float64	75.4%	2749.0 – 40000.0 (mean 13208.1518)
onions	float64	73.9%	350.0 – 10000.0 (mean 2306.7656)
potatoes	float64	67.7%	50.0 – 3051.85 (mean 683.6409)
rice	float64	70.7%	750.0 – 7250.0 (mean 2518.0398)
tomatoes	float64	72.9%	204.5 – 7000.0 (mean 1496.9644)
o_bananas	float64	0.0%	113.78 – 3520.83 (mean 646.6081)
h_bananas	float64	0.0%	125.41 – 4010.35 (mean 705.1058)
l_bananas	float64	0.0%
c_bananas	float64	0.0%
inflation_bananas	float64	5.2%
trust_bananas	float64	0.0%
o_beans	float64	0.0%
h_beans	float64	0.0%
l_beans	float64	0.0%
c_beans	float64	0.0%
inflation_beans	float64	5.2%
trust_beans	float64	0.0%
o_cassava_flour	float64	0.0%
h_cassava_flour	float64	0.0%
l_cassava_flour	float64	0.0%
c_cassava_flour	float64	0.0%
inflation_cassava_flour	float64	5.2%
trust_cassava_flour	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_flour	float64	0.0%
h_maize_flour	float64	0.0%
l_maize_flour	float64	0.0%
c_maize_flour	float64	0.0%
inflation_maize_flour	float64	5.2%
trust_maize_flour	float64	0.0%
o_meat_goat	float64	0.0%
h_meat_goat	float64	0.0%
l_meat_goat	float64	0.0%
c_meat_goat	float64	0.0%
inflation_meat_goat	float64	5.2%
trust_meat_goat	float64	0.0%
o_onions	float64	0.0%
h_onions	float64	0.0%
l_onions	float64	0.0%
c_onions	float64	0.0%
inflation_onions	float64	5.2%
trust_onions	float64	0.0%
o_potatoes	float64	0.0%
h_potatoes	float64	0.0%
l_potatoes	float64	0.0%
c_potatoes	float64	0.0%
inflation_potatoes	float64	5.2%
trust_potatoes	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_tomatoes	float64	0.0%
h_tomatoes	float64	0.0%
l_tomatoes	float64	0.0%
c_tomatoes	float64	0.0%
inflation_tomatoes	float64	5.2%
trust_tomatoes	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	-4.31	-2.44	-3.2872	-3.27
lon	29.08	30.78	29.8661	29.83
year	2007.0	2026.0	2016.1299	2016.0
month	1.0	12.0	6.4416	6.0
data_coverage	46.51	46.51	46.51	46.51
data_coverage_recent	82.16	82.16	82.16	82.16
index_confidence_score	0.91	0.91	0.91	0.91
spatially_interpolated	0.0	0.0	0.0	0.0
bananas	141.67	5000.0	974.7673	820.0
beans	400.0	4396.67	1662.5941	1300.0
cassava_flour	250.0	2701.59	1106.2669	1000.0
maize	226.0	3081.82	1176.9644	1066.67
maize_flour	460.0	4866.67	1491.7361	1373.4
meat_goat	2749.0	40000.0	13208.1518	10000.0
onions	350.0	10000.0	2306.7656	1867.71

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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. 763 column(s) with >80% missing values were removed: apples, beans_egyptian, beans_fao, bread, bread_fao, bulgur.... 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: bananas, beans, cassava_flour, maize, maize_flour, meat_goat, onions, potatoes....
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

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