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

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- energy
- food-security
- bfa
pretty_name: "Burkina Faso - Real Time Prices"
dataset_info:
splits:
- name: train
num_examples: 12012
- name: test
num_examples: 3003
---

# Burkina Faso - Real Time Prices

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/burkina-faso-real-time-prices) · **License:** `cc-by` · **Updated:** 2026-04-01

---

## 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](https://huggingface.co/electricsheepafrica).*

---

## 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 |

---

## 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.

---

## Quick Start

```python
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()
```

---

## 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 |

---

## 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 |

---

## 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.

---

## 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](https://data.humdata.org/dataset/burkina-faso-real-time-prices) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@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)}
}
```

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*