electricsheepafrica/africa-world-bank-public-sector-indicators-for-equatorial-guinea

---
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-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- economics
- indicators
- gnq
pretty_name: "Equatorial Guinea - Public Sector"
dataset_info:
splits:
- name: train
num_examples: 1420
- name: test
num_examples: 355
---

# Equatorial Guinea - Public Sector

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-public-sector-indicators-for-equatorial-guinea) · **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-equatorial-guinea) on HDX.

Effective governments improve people's standard of living by ensuring access to essential services – health, education, water and sanitation, electricity, transport – and the opportunity to live and work in peace and security. Data here includes World Bank staff assessments of country performance in economic management, structural policies, policies for social inclusion and equity, and public sector management and institutions for the poorest countries. Also included are indicators on revenues and expenses from the International Monetary Fund's Government Finance Statistics, and on tax policies from various sources.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: **GNQ**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Public health |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 1,776 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 1,420 rows |
| **Test split** | 355 rows |
| **Geographic scope** | GNQ |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-03-27 |

---

## Variables

**Geographic** — `country_name` (Equatorial Guinea), `country_iso3` (GNQ), `year` (range 1978.0–2024.0).

**Outcome / Measurement** — `value` (range -875867000000.0–3208068133000.0).

**Identifier / Metadata** — `indicator_name` (Armed forces personnel, total, Armed forces personnel (% of total labor force), Proportion of seats held by women in national parliaments (%)), `indicator_code` (MS.MIL.TOTL.P1, MS.MIL.TOTL.TF.ZS, SG.GEN.PARL.ZS), `esa_source` (HDX), `esa_processed` (2026-04-16).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-public-sector-indicators-for-equatorial-guinea")
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% | Equatorial Guinea |
| `country_iso3` | object | 0.0% | GNQ |
| `year` | int64 | 0.0% | 1978.0 – 2024.0 (mean 2011.951) |
| `indicator_name` | object | 0.0% | Armed forces personnel, total, Armed forces personnel (% of total labor force), Proportion of seats held by women in national parliaments (%) |
| `indicator_code` | object | 0.0% | MS.MIL.TOTL.P1, MS.MIL.TOTL.TF.ZS, SG.GEN.PARL.ZS |
| `value` | float64 | 0.0% | -875867000000.0 – 3208068133000.0 (mean 72617033776.2523) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-16 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1978.0 | 2024.0 | 2011.951 | 2013.0 |
| `value` | -875867000000.0 | 3208068133000.0 | 72617033776.2523 | 7.0 |

---

## 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`. 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-public-sector-indicators-for-equatorial-guinea) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_world_bank_public_sector_indicators_for_equatorial_guinea,
title = {Equatorial Guinea - Public Sector},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-public-sector-indicators-for-equatorial-guinea},
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.*