electricsheepafrica/africa-world-bank-combined-indicators-for-ethiopia

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- tabular-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- agriculture-livestock
- aid-effectiveness
- climate-weather
- development
- economics
- education
- energy
- environment
- eth
pretty_name: "Ethiopia - Economic, Social, Environmental, Health, Education, Development and Energy"
dataset_info:
splits:
- name: train
num_examples: 43874
- name: test
num_examples: 10968
---

# Ethiopia - Economic, Social, Environmental, Health, Education, Development and Energy

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

---

## Abstract

Contains data from the World Bank's [data portal](http://data.worldbank.org/) covering the following topics which also exist as individual datasets on HDX: [Agriculture and Rural Development](https://data.humdata.org/dataset/world-bank-agriculture-and-rural-development-indicators-for-ethiopia), [Aid Effectiveness](https://data.humdata.org/dataset/world-bank-aid-effectiveness-indicators-for-ethiopia), [Economy and Growth](https://data.humdata.org/dataset/world-bank-economy-and-growth-indicators-for-ethiopia), [Education](https://data.humdata.org/dataset/world-bank-education-indicators-for-ethiopia), [Energy and Mining](https://data.humdata.org/dataset/world-bank-energy-and-mining-indicators-for-ethiopia), [Environment](https://data.humdata.org/dataset/world-bank-environment-indicators-for-ethiopia), [Financial Sector](https://data.humdata.org/dataset/world-bank-financial-sector-indicators-for-ethiopia), [Health](https://data.humdata.org/dataset/world-bank-health-indicators-for-ethiopia), [Infrastructure](https://data.humdata.org/dataset/world-bank-infrastructure-indicators-for-ethiopia), [Social Protection and Labor](https://data.humdata.org/dataset/world-bank-social-protection-and-labor-indicators-for-ethiopia), [Poverty](https://data.humdata.org/dataset/world-bank-poverty-indicators-for-ethiopia), [Private Sector](https://data.humdata.org/dataset/world-bank-private-sector-indicators-for-ethiopia), [Public Sector](https://data.humdata.org/dataset/world-bank-public-sector-indicators-for-ethiopia), [Science and Technology](https://data.humdata.org/dataset/world-bank-science-and-technology-indicators-for-ethiopia), [Social Development](https://data.humdata.org/dataset/world-bank-social-development-indicators-for-ethiopia), [Urban Development](https://data.humdata.org/dataset/world-bank-urban-development-indicators-for-ethiopia), [Gender](https://data.humdata.org/dataset/world-bank-gender-indicators-for-ethiopia), [Millenium development goals](https://data.humdata.org/dataset/world-bank-millenium-development-goals-indicators-for-ethiopia), [Climate Change](https://data.humdata.org/dataset/world-bank-climate-change-indicators-for-ethiopia), [External Debt](https://data.humdata.org/dataset/world-bank-external-debt-indicators-for-ethiopia), [Trade](https://data.humdata.org/dataset/world-bank-trade-indicators-for-ethiopia).

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

*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)** | 54,843 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 43,874 rows |
| **Test split** | 10,968 rows |
| **Geographic scope** | ETH |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-03-27 |

---

## Variables

**Geographic** — `country_name` (Ethiopia), `country_iso3` (ETH), `year` (range 1960.0–2025.0).

**Outcome / Measurement** — `value` (range -745007508500.0–12529568957400.0).

**Identifier / Metadata** — `indicator_name` (Domestic credit to private sector (% of GDP), Population in urban agglomerations of more than 1 million, Net migration), `indicator_code` (EN.URB.MCTY.TL.ZS, EN.URB.MCTY, SM.POP.NETM), `esa_source` (HDX), `esa_processed` (2026-04-10).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-combined-indicators-for-ethiopia")
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% | Ethiopia |
| `country_iso3` | object | 0.0% | ETH |
| `year` | int64 | 0.0% | 1960.0 – 2025.0 (mean 2001.3622) |
| `indicator_name` | object | 0.0% | Domestic credit to private sector (% of GDP), Population in urban agglomerations of more than 1 million, Net migration |
| `indicator_code` | object | 0.0% | EN.URB.MCTY.TL.ZS, EN.URB.MCTY, SM.POP.NETM |
| `value` | float64 | 0.0% | -745007508500.0 – 12529568957400.0 (mean 15703411148.0665) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-10 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1960.0 | 2025.0 | 2001.3622 | 2004.0 |
| `value` | -745007508500.0 | 12529568957400.0 | 15703411148.0665 | 44.9136 |

---

## 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`. 16,165 exact duplicate rows were removed. 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-combined-indicators-for-ethiopia) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_world_bank_combined_indicators_for_ethiopia,
title = {Ethiopia - Economic, Social, Environmental, Health, Education, Development and Energy},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-combined-indicators-for-ethiopia},
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.*

搜集汇总

数据集介绍

构建方式

在数据科学领域，构建高质量数据集是支撑实证研究的基础。该数据集由世界银行集团发布，原始数据通过HDX平台的CKAN API获取，涵盖了埃塞俄比亚在农业、经济、教育、能源、环境、健康等二十余个关键发展领域的指标。数据经过系统化清洗，包括统一缺失值标记、去除重复记录，并采用固定随机种子将数据划分为训练集与测试集，最终以Snappy压缩的Parquet格式存储，确保了数据的完整性与机器学习任务的适用性。

特点

作为聚焦国家发展指标的数据集，其特点体现在多维度的观测视角与结构化设计。数据集包含54,843条记录，每条记录代表国家层面的聚合数据，时间跨度从1960年至2025年，覆盖了广泛的社会经济与环境指标。数据以表格形式组织，包含地理标识、年份、指标名称与数值等八个字段，兼具数值与分类变量，且无缺失值，为跨领域比较分析与时间序列建模提供了稳定而丰富的素材。

使用方法

在应用层面，该数据集适用于表格分类等机器学习任务，能够支持发展政策评估与趋势预测研究。使用者可通过Hugging Face的datasets库直接加载数据，并便捷地转换为Pandas DataFrame进行探索性分析。数据已预分为训练集与测试集，便于直接投入模型训练与验证。研究人员可依据指标代码与年份维度，深入挖掘埃塞俄比亚在特定领域的发展轨迹，或结合其他区域数据进行对比分析。

背景与挑战

背景概述

在全球化与可持续发展议程交织的背景下，对特定国家多维发展指标的整合分析成为理解区域进步与挑战的关键。世界银行集团作为国际发展数据的重要权威机构，系统收集并发布了涵盖经济、社会、环境、健康、教育及能源等广泛领域的国家层面指标。Electric Sheep Africa于2026年对该数据进行重新整理与标准化，构建了针对埃塞俄比亚的机器学习就绪数据集，旨在为研究人员和政策制定者提供一个跨领域、长时间序列的结构化数据资源，以支持对国家发展轨迹的量化建模与预测分析。

当前挑战

该数据集致力于解决对国家综合发展状况进行多维度建模与预测的复杂问题，其核心挑战在于如何从高度异质且动态变化的指标中提取稳健且可解释的模式。具体而言，指标间存在显著的尺度差异与定义不一致性，例如经济总量指标与百分比指标共存，增加了特征归一化与模型训练的难度。在构建过程中，数据清洗面临重复条目识别与缺失值统一处理的挑战，原始数据中可能存在报告偏差与方法论差异，自动化流程难以纠正深层的概念不一致或抽样偏差，这要求使用者谨慎理解数据来源的固有局限与背景假设。

常用场景

经典使用场景

在非洲发展经济学与公共政策研究领域，该数据集作为埃塞俄比亚国家层面的综合指标库，其经典使用场景集中于多维度时间序列分析。研究者常利用其涵盖经济、社会、环境、健康、教育等领域的结构化数据，构建计量经济模型，以评估国家发展政策的长期效应。例如，通过分析1960年至2025年的年度指标变化，能够系统追踪城市化进程、信贷扩张或人口迁移等关键变量的动态轨迹，为理解埃塞俄比亚的发展路径提供实证基础。

实际应用

在实际应用层面，该数据集为国际组织、政府机构及非营利组织提供了决策支持工具。基于其指标，可开发预警系统以监测经济脆弱性或社会不平等趋势；在公共卫生领域，能够辅助资源分配优化，如依据健康指标预测医疗设施需求。此外，数据驱动的项目评估框架得以构建，帮助援助方量化发展干预措施在能源接入、教育公平等方面的实际成效，提升政策执行的精准度。

衍生相关工作

围绕该数据集衍生的经典工作包括基于机器学习的国家发展预测模型，例如使用时序特征预测贫困率或经济增长拐点。在学术文献中，它常被引用于跨国比较研究，探讨埃塞俄比亚在非洲语境下的发展异质性。相关方法学进展亦得以推动，如针对高维稀疏面板数据的插补算法，或融合多源指标的可解释性人工智能框架，这些工作显著丰富了发展计量学的工具箱。