electricsheepafrica/africa-wfp-food-prices-for-mauritania

---
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
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- economics
- food-security
- indicators
- markets
- mrt
pretty_name: "Mauritania - Food Prices"
dataset_info:
splits:
- name: train
num_examples: 12647
- name: test
num_examples: 3161
---

# Mauritania - Food Prices

**Publisher:** WFP - World Food Programme · **Source:** [HDX](https://data.humdata.org/dataset/wfp-food-prices-for-mauritania) · **License:** `cc-by-igo` · **Updated:** 2026-04-05

---

## Abstract

This dataset contains Food Prices data for Mauritania, sourced from the World Food Programme Price Database. The World Food Programme Price Database covers foods such as maize, rice, beans, fish, and sugar for 98 countries and some 3000 markets. It is updated weekly but contains to a large extent monthly data. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.

Each row in this dataset represents subnational administrative unit observations. Temporal coverage is indicated by the `date` column(s). Geographic scope: **MRT**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Food security and nutrition |
| **Unit of observation** | Subnational administrative unit observations |
| **Rows (total)** | 15,809 |
| **Columns** | 18 (6 numeric, 11 categorical, 1 datetime) |
| **Train split** | 12,647 rows |
| **Test split** | 3,161 rows |
| **Geographic scope** | MRT |
| **Publisher** | WFP - World Food Programme |
| **HDX last updated** | 2026-04-05 |

---

## Variables

**Geographic** — `admin1` (Brakna, Assaba, Adrar), `admin2` (Tintane, Barkeol, Nouadhibou), `latitude` (range 14.77–22.69), `longitude` (range -17.05–-5.52), `category` (cereals and tubers, meat, fish and eggs, miscellaneous food) and 4 others.

**Temporal** — `date`.

**Outcome / Measurement** — `priceflag` (actual, aggregate, actual,aggregate), `price` (range 2.8–40003.85), `usdprice` (range 0.1–1166.97).

**Identifier / Metadata** — `market_id` (range 689.0–1638.0), `esa_source` (HDX), `esa_processed`.

**Other** — `market` (Barkéol, Nouadhibou, Ndiago), `unit` (KG, Head, L).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-wfp-food-prices-for-mauritania")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `date` | datetime64[ns] | 0.0% | |
| `admin1` | object | 0.0% | Brakna, Assaba, Adrar |
| `admin2` | object | 0.0% | Tintane, Barkeol, Nouadhibou |
| `market` | object | 0.0% | Barkéol, Nouadhibou, Ndiago |
| `market_id` | int64 | 0.0% | 689.0 – 1638.0 (mean 1381.0374) |
| `latitude` | float64 | 0.0% | 14.77 – 22.69 (mean 17.3775) |
| `longitude` | float64 | 0.0% | -17.05 – -5.52 (mean -13.0519) |
| `category` | object | 0.0% | cereals and tubers, meat, fish and eggs, miscellaneous food |
| `commodity` | object | 0.0% | Sugar, Oil (vegetable), Rice (imported) |
| `commodity_id` | int64 | 0.0% | 51.0 – 1310.0 (mean 273.6876) |
| `unit` | object | 0.0% | KG, Head, L |
| `priceflag` | object | 0.0% | actual, aggregate, actual,aggregate |
| `pricetype` | object | 0.0% | Retail |
| `currency` | object | 0.0% | MRU |
| `price` | float64 | 0.0% | 2.8 – 40003.85 (mean 1048.6282) |
| `usdprice` | float64 | 0.0% | 0.1 – 1166.97 (mean 27.5536) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `market_id` | 689.0 | 1638.0 | 1381.0374 | 1620.0 |
| `latitude` | 14.77 | 22.69 | 17.3775 | 16.62 |
| `longitude` | -17.05 | -5.52 | -13.0519 | -13.05 |
| `commodity_id` | 51.0 | 1310.0 | 273.6876 | 97.0 |
| `price` | 2.8 | 40003.85 | 1048.6282 | 36.96 |
| `usdprice` | 0.1 | 1166.97 | 27.5536 | 1.0 |

---

## 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`. 1 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 WFP - World Food Programme and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/wfp-food-prices-for-mauritania) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_wfp_food_prices_for_mauritania,
title = {Mauritania - Food Prices},
author = {WFP - World Food Programme},
year = {2026},
url = {https://data.humdata.org/dataset/wfp-food-prices-for-mauritania},
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.*