electricsheepafrica/africa-world-bank-energy-and-mining-indicators-for-zimbabwe

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- development
- energy
- indicators
- zwe
pretty_name: "Zimbabwe - Energy and Mining"
dataset_info:
splits:
- name: train
num_examples: 1112
- name: test
num_examples: 278
---

# Zimbabwe - Energy and Mining

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-energy-and-mining-indicators-for-zimbabwe) · **License:** `cc-by` · **Updated:** 2026-03-27

---

## 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-zimbabwe) 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 2026-03-27. Geographic scope: **ZWE**.

*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)** | 1,391 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 1,112 rows |
| **Test split** | 278 rows |
| **Geographic scope** | ZWE |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-03-27 |

---

## Variables

**Geographic** — `country_name` (Zimbabwe), `country_iso3` (ZWE), `year` (range 1970.0–2024.0).

**Outcome / Measurement** — `value` (range 0.0–1608360000.0).

**Identifier / Metadata** — `indicator_name` (Adjusted savings: mineral depletion (current US$), Adjusted savings: mineral depletion (% of GNI), Total natural resources rents (% of GDP)), `indicator_code` (NY.ADJ.DMIN.CD, NY.ADJ.DMIN.GN.ZS, NY.GDP.TOTL.RT.ZS), `esa_source` (HDX), `esa_processed` (2026-04-10).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-energy-and-mining-indicators-for-zimbabwe")
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% | Zimbabwe |
| `country_iso3` | object | 0.0% | ZWE |
| `year` | int64 | 0.0% | 1970.0 – 2024.0 (mean 2002.6794) |
| `indicator_name` | object | 0.0% | Adjusted savings: mineral depletion (current US$), Adjusted savings: mineral depletion (% of GNI), Total natural resources rents (% of GDP) |
| `indicator_code` | object | 0.0% | NY.ADJ.DMIN.CD, NY.ADJ.DMIN.GN.ZS, NY.GDP.TOTL.RT.ZS |
| `value` | float64 | 0.0% | 0.0 – 1608360000.0 (mean 9671475.4676) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-10 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1970.0 | 2024.0 | 2002.6794 | 2004.0 |
| `value` | 0.0 | 1608360000.0 | 9671475.4676 | 10.9788 |

---

## 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`. 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-zimbabwe) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_world_bank_energy_and_mining_indicators_for_zimbabwe,
title = {Zimbabwe - Energy and Mining},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-energy-and-mining-indicators-for-zimbabwe},
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.*