electricsheepafrica/africa-world-bank-energy-and-mining-indicators-for-federal-republic-of-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-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- development
- energy
- hxl
- indicators
- som
pretty_name: "Federal Republic of Somalia - Energy and Mining"
dataset_info:
splits:
- name: train
num_examples: 516
- name: test
num_examples: 129
---

# Federal Republic of Somalia - Energy and Mining

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-energy-and-mining-indicators-for-federal-republic-of-somalia) · **License:** `cc-by` · **Updated:** 2025-11-04

---

## Abstract

Contains data from the World Bank's [data portal](http://data.worldbank.org/). There is also a [consolidated country dataset](https://data.humdata.org/dataset/world-bank-combined-indicators-for-federal-republic-of-somalia) on HDX.

The world economy needs ever-increasing amounts of energy to sustain economic growth, raise living standards, and reduce poverty. But today's trends in energy use are not sustainable. As the world's population grows and economies become more industrialized, nonrenewable energy sources will become scarcer and more costly. Data here on energy production, use, dependency, and efficiency are compiled by the World Bank from the International Energy Agency and the Carbon Dioxide Information Analysis Center.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2025-11-04. Geographic scope: **SOM**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Demographics and population |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 646 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 516 rows |
| **Test split** | 129 rows |
| **Geographic scope** | SOM |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2025-11-04 |

---

## Variables

**Geographic** — `country_name` (Federal Republic of Somalia, #country+name), `country_iso3` (SOM, #country+code), `year` (range 1962.0–2023.0).

**Outcome / Measurement** — `value` (range 0.0–42940000.0).

**Identifier / Metadata** — `indicator_name` (Adjusted savings: mineral depletion (current US$), Adjusted savings: energy depletion (current US$), Renewable energy consumption (% of total final energy consumption)), `indicator_code` (NY.ADJ.DMIN.CD, NY.ADJ.DNGY.CD, EG.FEC.RNEW.ZS), `esa_source` (HDX), `esa_processed` (2026-04-08).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-energy-and-mining-indicators-for-federal-republic-of-somalia")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `country_name` | object | 0.0% | Federal Republic of Somalia, #country+name |
| `country_iso3` | object | 0.0% | SOM, #country+code |
| `year` | float64 | 0.2% | 1962.0 – 2023.0 (mean 2001.2202) |
| `indicator_name` | object | 0.0% | Adjusted savings: mineral depletion (current US$), Adjusted savings: energy depletion (current US$), Renewable energy consumption (% of total final energy consumption) |
| `indicator_code` | object | 0.0% | NY.ADJ.DMIN.CD, NY.ADJ.DNGY.CD, EG.FEC.RNEW.ZS |
| `value` | float64 | 0.2% | 0.0 – 42940000.0 (mean 247520.9607) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-08 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1962.0 | 2023.0 | 2001.2202 | 2006.0 |
| `value` | 0.0 | 42940000.0 | 247520.9607 | 0.0 |

---

## 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`. 2 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.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/world-bank-energy-and-mining-indicators-for-federal-republic-of-somalia) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_world_bank_energy_and_mining_indicators_for_federal_republic_of_somalia,
title = {Federal Republic of Somalia - Energy and Mining},
author = {World Bank Group},
year = {2025},
url = {https://data.humdata.org/dataset/world-bank-energy-and-mining-indicators-for-federal-republic-of-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.*