electricsheepafrica/africa-mali-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
- mli
pretty_name: "Mali - Real Time Prices"
dataset_info:
splits:
- name: train
num_examples: 24392
- name: test
num_examples: 6098
---

# Mali - Real Time Prices

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/mali-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: **MLI**.

*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)** | 30,491 |
| **Columns** | 85 (72 numeric, 10 categorical, 3 datetime) |
| **Train split** | 24,392 rows |
| **Test split** | 6,098 rows |
| **Geographic scope** | MLI |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-04-01 |

---

## Variables

**Geographic** — `iso3` (MLI), `country` (Mali), `lat` (range 10.92–20.2), `lon` (range -11.43–3.02), `year` (range 2007.0–2026.0) and 19 others.

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

**Demographic** — `data_coverage` (range 24.98–24.98), `data_coverage_recent` (range 16.74–16.74).

**Identifier / Metadata** — `adm1_name` (Mopti, Tombouctou, Segou), `adm2_name` (Bamako, Kidal, Douentza), `mkt_name` (Abeibara, Niarela, Niafunké), `geo_id` (gid_18440000014200000, gid_126500000-79800000, gid_159300000-39800000), `esa_source` (HDX) and 1 others.

**Other** — `components` (beans (1 KG, Index Weight = 2), groundnuts (1 KG, Index Weight = 2), maize (1 KG, Index Weight = 2), millet (1 KG, Index Weight = 1), millet_fao (100 kg, Index Weight = 0.01), rice (1 KG, Index Weight = 1), rice_fao (100 kg, Index Weight = 0.01), sorghum (1 KG, Index Weight = 1), sorghum_fao (100 kg, Index Weight = 0.01)), `start_dense_data`, `maize` (range 60.0–1000.0), `millet` (range 74.38–1000.0), `rice` (range 120.0–1000.0) and 46 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mali-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% | MLI |
| `country` | object | 0.0% | Mali |
| `adm1_name` | object | 0.0% | Mopti, Tombouctou, Segou |
| `adm2_name` | object | 0.0% | Bamako, Kidal, Douentza |
| `mkt_name` | object | 0.0% | Abeibara, Niarela, Niafunké |
| `lat` | float64 | 0.8% | 10.92 – 20.2 (mean 14.5228) |
| `lon` | float64 | 0.8% | -11.43 – 3.02 (mean -4.6812) |
| `geo_id` | object | 0.0% | gid_18440000014200000, gid_126500000-79800000, gid_159300000-39800000 |
| `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 6.4415) |
| `currency` | object | 0.0% | XOF |
| `components` | object | 0.0% | beans (1 KG, Index Weight = 2), groundnuts (1 KG, Index Weight = 2), maize (1 KG, Index Weight = 2), millet (1 KG, Index Weight = 1), millet_fao (100 kg, Index Weight = 0.01), rice (1 KG, Index Weight = 1), rice_fao (100 kg, Index Weight = 0.01), 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% | 24.98 – 24.98 (mean 24.98) |
| `data_coverage_recent` | float64 | 0.0% | 16.74 – 16.74 (mean 16.74) |
| `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) |
| `maize` | float64 | 75.7% | 60.0 – 1000.0 (mean 200.7756) |
| `millet` | float64 | 76.7% | 74.38 – 1000.0 (mean 229.9995) |
| `rice` | float64 | 65.3% | 120.0 – 1000.0 (mean 384.5701) |
| `sorghum` | float64 | 68.4% | 67.5 – 950.0 (mean 210.2408) |
| `o_beans` | float64 | 0.0% | 206.58 – 985.19 (mean 472.5562) |
| `h_beans` | float64 | 0.0% | 216.36 – 1082.76 (mean 494.8293) |
| `l_beans` | float64 | 0.0% | 197.04 – 928.38 (mean 450.9479) |
| `c_beans` | float64 | 0.0% | 206.58 – 985.72 (mean 473.5239) |
| `inflation_beans` | float64 | 5.2% | -53.85 – 127.09 (mean 5.9594) |
| `trust_beans` | float64 | 0.0% | 8.8 – 10.0 (mean 9.323) |
| `o_groundnuts` | float64 | 0.0% | 296.07 – 1472.73 (mean 673.1534) |
| `h_groundnuts` | float64 | 0.0% | 311.63 – 1561.08 (mean 704.5697) |
| `l_groundnuts` | float64 | 0.0% | |
| `c_groundnuts` | float64 | 0.0% | |
| `inflation_groundnuts` | float64 | 5.2% | |
| `trust_groundnuts` | 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_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` | 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_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% | 2026-04-05 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `lat` | 10.92 | 20.2 | 14.5228 | 14.45 |
| `lon` | -11.43 | 3.02 | -4.6812 | -4.9 |
| `year` | 2007.0 | 2026.0 | 2016.1301 | 2016.0 |
| `month` | 1.0 | 12.0 | 6.4415 | 6.0 |
| `data_coverage` | 24.98 | 24.98 | 24.98 | 24.98 |
| `data_coverage_recent` | 16.74 | 16.74 | 16.74 | 16.74 |
| `index_confidence_score` | 0.98 | 0.98 | 0.98 | 0.98 |
| `spatially_interpolated` | 0.0 | 0.0 | 0.0 | 0.0 |
| `maize` | 60.0 | 1000.0 | 200.7756 | 187.0 |
| `millet` | 74.38 | 1000.0 | 229.9995 | 210.0 |
| `rice` | 120.0 | 1000.0 | 384.5701 | 375.0 |
| `sorghum` | 67.5 | 950.0 | 210.2408 | 200.0 |
| `o_beans` | 206.58 | 985.19 | 472.5562 | 442.79 |
| `h_beans` | 216.36 | 1082.76 | 494.8293 | 460.76 |
| `l_beans` | 197.04 | 928.38 | 450.9479 | 425.13 |

---

## 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`. 775 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.

---

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

---

## Citation

```bibtex
@dataset{hdx_africa_mali_real_time_prices,
title = {Mali - Real Time Prices},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/mali-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.*