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.*

annotations_creators:
- 无注释
language_creators:
- 采集自现有文本
language:
- 英语
license: cc-by-4.0
multilinguality:
- 单语言
size_categories:
- 1000 < 样本数 < 10000
source_datasets:
- 原创数据集
task_categories:
- 表格分类
task_ids: []
tags:
- 非洲
- 人道主义
- HDX（Humanitarian Data Exchange）
- Electric Sheep Africa
- 经济学
- 指标
- GNQ（赤道几内亚ISO 3代码）
pretty_name: "赤道几内亚 - 公共部门"
dataset_info:
splits:
- name: 训练集
num_examples: 1420
- name: 测试集
num_examples: 355

# 赤道几内亚 - 公共部门

**发布方**：世界银行集团 · **来源**：[HDX（Humanitarian Data Exchange）](https://data.humdata.org/dataset/world-bank-public-sector-indicators-for-equatorial-guinea) · **许可证**：`cc-by` · **更新时间**：2026-03-27

---

## 摘要
本数据集包含来自世界银行[数据门户](http://data.worldbank.org/)的公开数据。HDX平台上同时提供一份[赤道几内亚整合国家数据集](https://data.humdata.org/dataset/world-bank-combined-indicators-for-equatorial-guinea)。

高效的政府通过保障民众获得基本服务——医疗、教育、供水与卫生设施、电力、交通——以及在和平与安全的环境中生活和工作的机会，来提升人民的生活水平。本数据集收录了世界银行工作人员针对最贫困国家在经济管理、结构性政策、社会包容与公平政策、公共部门管理与制度建设等方面的国家绩效评估数据。同时还包含来自国际货币基金组织政府财政统计的收支指标，以及多渠道来源的税收政策相关数据。

本数据集的每一行均代表国家级汇总统计数据。本数据集在HDX平台的最后更新时间为2026-03-27。地理覆盖范围：**GNQ（赤道几内亚ISO 3代码）**。

*本数据集已由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为适用于机器学习的Parquet格式。*

---

## 数据集特征
| | |
|---|---|
| **领域** | 公共卫生 |
| **观测单元** | 国家级汇总统计数据 |
| **总样本行数** | 1,776 |
| **列数** | 8列（2列数值型，6列分类型，0列日期时间型） |
| **训练集划分** | 1,420行 |
| **测试集划分** | 355行 |
| **地理覆盖范围** | GNQ |
| **发布方** | 世界银行集团 |
| **HDX平台最后更新时间** | 2026-03-27 |

---

## 变量说明
**地理类变量** — `country_name`（国家名称：赤道几内亚）、`country_iso3`（国家ISO 3代码：GNQ）、`year`（年份范围：1978.0–2024.0）。

**结果/测量变量** — `value`（数值范围：-875867000000.0–3208068133000.0）。

**标识符/元数据变量** — `indicator_name`（指标名称：武装部队总人数、武装部队人员占总劳动力比例、各国议会女性议员席位占比（%））、`indicator_code`（指标代码：MS.MIL.TOTL.P1、MS.MIL.TOTL.TF.ZS、SG.GEN.PARL.ZS）、`esa_source`（数据来源：HDX）、`esa_processed`（数据处理时间：2026-04-16）。

---

## 快速入门
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()

---

## 数据结构
| 列名 | 数据类型 | 缺失率 | 范围/示例值 |
|---|---|---|---|
| `country_name` | object | 0.0% | 赤道几内亚 |
| `country_iso3` | object | 0.0% | GNQ |
| `year` | int64 | 0.0% | 1978.0 – 2024.0（均值：2011.951） |
| `indicator_name` | object | 0.0% | 武装部队总人数、武装部队人员占总劳动力比例、各国议会女性议员席位占比（%） |
| `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（均值：72617033776.2523） |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-16 |

---

## 数值型变量统计摘要
| 列名 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|---|
| `year` | 1978.0 | 2024.0 | 2011.951 | 2013.0 |
| `value` | -875867000000.0 | 3208068133000.0 | 72617033776.2523 | 7.0 |

---

## 数据整理流程
原始数据通过CKAN API从HDX平台下载，并转换为Parquet格式。列名统一转为小写并标准化为蛇形命名法（snake_case）。常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）被统一替换为`NaN`。本数据集以80:20的比例划分为训练集与测试集，划分过程使用固定随机种子（42），最终以Snappy压缩的Parquet格式存储。

---

## 数据集局限性
- 本数据集源自世界银行集团，未经过Electric Sheep Africa的独立验证。
- 自动化数据清洗无法修正原始数据收集中的错报值、定义不一致或抽样偏差问题。
- 如需了解发布方的方法说明与免责条款，请参阅[HDX平台原始数据集页面](https://data.humdata.org/dataset/world-bank-public-sector-indicators-for-equatorial-guinea)。

---

## 引用格式
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) — 非洲机器学习数据集基础设施。尼日利亚拉各斯。*