electricsheepafrica/africa-faostat-food-security-indicators-for-somalia

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- food-security
- indicators
- nutrition
- som
pretty_name: "Somalia - Food Security and Nutrition Indicators"
dataset_info:
splits:
- name: train
num_examples: 778
- name: test
num_examples: 194
---

# Somalia - Food Security and Nutrition Indicators

**Publisher:** Food and Agriculture Organization (FAO) of the United Nations · **Source:** [HDX](https://data.humdata.org/dataset/faostat-food-security-indicators-for-somalia) · **License:** `cc-by-igo` · **Updated:** 2026-04-06

---

## Abstract

Food Security and Nutrition Indicators for Somalia.

Contains data from the FAOSTAT [bulk data service](https://fenixservices.fao.org/faostat/static/bulkdownloads/datasets_E.json).

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the `startdate`, `enddate` column(s). Geographic scope: **SOM**.

*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)** | 973 |
| **Columns** | 17 (5 numeric, 10 categorical, 2 datetime) |
| **Train split** | 778 rows |
| **Test split** | 194 rows |
| **Geographic scope** | SOM |
| **Publisher** | Food and Agriculture Organization (FAO) of the United Nations |
| **HDX last updated** | 2026-04-06 |

---

## Variables

**Geographic** — `iso3` (SOM), `year_code` (range 2000.0–20222024.0), `year` (range 2000.0–2024.0).

**Temporal** — `startdate`, `enddate`.

**Outcome / Measurement** — `value` (range -3.31–2190.0).

**Identifier / Metadata** — `area_code` (range 201.0–201.0), `area_code_m49` ('706), `item_code` (210071M, 210081F, 210081M), `element_code` (range 6121.0–61322.0), `esa_source` (HDX) and 1 others.

**Other** — `area` (Somalia), `item` (Number of severely food insecure male adults (million) (3-year average), Number of moderately or severely food insecure female adults (million) (3-year average), Number of moderately or severely food insecure male adults (million) (3-year average)), `element` (Value, Confidence interval: Lower bound, Confidence interval: Upper bound), `unit` (%, million No, kcal/cap/d), `flag` (E, X, O).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-faostat-food-security-indicators-for-somalia")
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% | SOM |
| `startdate` | datetime64[ns] | 0.0% | |
| `enddate` | datetime64[ns] | 0.0% | |
| `area_code` | int64 | 0.0% | 201.0 – 201.0 (mean 201.0) |
| `area_code_m49` | object | 0.0% | '706 |
| `area` | object | 0.0% | Somalia |
| `item_code` | object | 0.0% | 210071M, 210081F, 210081M |
| `item` | object | 0.0% | Number of severely food insecure male adults (million) (3-year average), Number of moderately or severely food insecure female adults (million) (3-year average), Number of moderately or severely food insecure male adults (million) (3-year average) |
| `element_code` | int64 | 0.0% | 6121.0 – 61322.0 (mean 18367.2939) |
| `element` | object | 0.0% | Value, Confidence interval: Lower bound, Confidence interval: Upper bound |
| `year_code` | int64 | 0.0% | 2000.0 – 20222024.0 (mean 10692169.0997) |
| `year` | int64 | 0.0% | 2000.0 – 2024.0 (mean 2014.9373) |
| `unit` | object | 2.3% | %, million No, kcal/cap/d |
| `value` | float64 | 32.2% | -3.31 – 2190.0 (mean 329.0557) |
| `flag` | object | 0.0% | E, X, O |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-10 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `area_code` | 201.0 | 201.0 | 201.0 | 201.0 |
| `element_code` | 6121.0 | 61322.0 | 18367.2939 | 6128.0 |
| `year_code` | 2000.0 | 20222024.0 | 10692169.0997 | 20042006.0 |
| `year` | 2000.0 | 2024.0 | 2014.9373 | 2017.0 |
| `value` | -3.31 | 2190.0 | 329.0557 | 30.0 |

---

## 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) with >80% missing values were removed: `note`. 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 Food and Agriculture Organization (FAO) of the United Nations 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: `value`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/faostat-food-security-indicators-for-somalia) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_faostat_food_security_indicators_for_somalia,
title = {Somalia - Food Security and Nutrition Indicators},
author = {Food and Agriculture Organization (FAO) of the United Nations},
year = {2026},
url = {https://data.humdata.org/dataset/faostat-food-security-indicators-for-somalia},
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.*