electricsheepafrica/africa-hdro-data-for-togo

---
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-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- demographics
- development
- education
- gender
- health
- indicators
- socioeconomics
- tgo
pretty_name: "Togo - Human Development Indicators"
dataset_info:
splits:
- name: train
num_examples: 704
- name: test
num_examples: 176
---

# Togo - Human Development Indicators

**Publisher:** UNDP Human Development Reports Office (HDRO) · **Source:** [HDX](https://data.humdata.org/dataset/hdro-data-for-togo) · **License:** `cc-by-igo` · **Updated:** 2026-03-04

---

## Abstract

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities.
The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population.
The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-04. Geographic scope: **TGO**.

*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)** | 881 |
| **Columns** | 10 (2 numeric, 8 categorical, 0 datetime) |
| **Train split** | 704 rows |
| **Test split** | 176 rows |
| **Geographic scope** | TGO |
| **Publisher** | UNDP Human Development Reports Office (HDRO) |
| **HDX last updated** | 2026-03-04 |

---

## Variables

**Geographic** — `country_code` (TGO), `country_name` (Togo), `index_id` (GDI, GII, HDI), `index_name` (Gender Development Index, Gender Inequality Index, Human Development Index), `year` (range 1990.0–2023.0).

**Outcome / Measurement** — `value` (range 0.18–3237.116).

**Identifier / Metadata** — `indicator_id` (eys, pr_f, eys_f), `indicator_name` (Expected Years of Schooling (years), Share of seats in parliament, female (% held by women), Expected Years of Schooling, female (years)), `esa_source` (HDX), `esa_processed` (2026-04-06).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-hdro-data-for-togo")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `country_code` | object | 0.0% | TGO |
| `country_name` | object | 0.0% | Togo |
| `indicator_id` | object | 0.0% | eys, pr_f, eys_f |
| `indicator_name` | object | 0.0% | Expected Years of Schooling (years), Share of seats in parliament, female (% held by women), Expected Years of Schooling, female (years) |
| `index_id` | object | 0.0% | GDI, GII, HDI |
| `index_name` | object | 0.0% | Gender Development Index, Gender Inequality Index, Human Development Index |
| `value` | float64 | 0.0% | 0.18 – 3237.116 (mean 228.8846) |
| `year` | int64 | 0.0% | 1990.0 – 2023.0 (mean 2008.4949) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-06 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `value` | 0.18 | 3237.116 | 228.8846 | 14.536 |
| `year` | 1990.0 | 2023.0 | 2008.4949 | 2010.0 |

---

## 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 UNDP Human Development Reports Office (HDRO) 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/hdro-data-for-togo) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_hdro_data_for_togo,
title = {Togo - Human Development Indicators},
author = {UNDP Human Development Reports Office (HDRO)},
year = {2026},
url = {https://data.humdata.org/dataset/hdro-data-for-togo},
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:
- 获取型
language:
- 英语
license: 知识共享署名4.0（cc-by-4.0）
multilinguality:
- 单语言
size_categories:
- 少于1000条
source_datasets:
- 原创数据集
task_categories:
- 表格分类
- 表格回归
task_ids: []
tags:
- 非洲
- 人道主义
- 人类数据交换（HDX）
- 非洲电羊（electric-sheep-africa）
- 人口统计
- 发展
- 教育
- 性别
- 健康
- 指标
- 社会经济
- 多哥（tgo）
pretty_name: "多哥 - 人类发展指标"
dataset_info:
splits:
- name: train
num_examples: 704
- name: test
num_examples: 176

# 多哥 - 人类发展指标

**发布方：** 联合国开发计划署人类发展报告办公室（UNDP Human Development Reports Office, HDRO） · **来源：** [HDX](https://data.humdata.org/dataset/hdx-data-for-togo) · **许可证：** `cc-by-igo` · **更新日期：** 2026-03-04

---

## 摘要

人类发展报告的宗旨是推动全球、区域与国家层面围绕与人类发展相关的政策议题展开务实讨论。据此，报告中的数据需达到最高的数据质量、一致性、国际可比性与透明度标准。人类发展报告办公室（HDRO）完全遵循国际统计活动治理原则。

人类发展指数（Human Development Index, HDI）旨在强调，人民及其能力应当成为评估国家发展的终极标准，而非仅以经济增长作为唯一衡量依据。HDI还可用于审视国家的政策选择，探讨为何两个人均国民总收入水平相同的国家最终会呈现出不同的人类发展结果。这类对比能够引发关于政府政策优先级的广泛讨论。
人类发展指数是对人类发展三大核心维度平均成就的综合衡量指标：健康长寿的生活、拥有充足知识以及体面的生活水平。HDI是三个维度归一化指数的几何平均值。

2019年全球多维贫困指数（Multidimensional Poverty Index, MPI）数据揭示了区域、国家与次国家层面处于贫困状态的人口规模，并展现了国家间以及贫困人口内部的不平等状况。该指数由联合国开发计划署（United Nations Development Programme, UNDP）与牛津大学牛津贫困与人类发展倡议（Oxford Poverty and Human Development Initiative, OPHI）联合开发，2019年全球MPI涵盖101个国家，覆盖全球76%的人口。
MPI提供了全球贫困全方位的全面且深入的图景，并监测可持续发展目标（Sustainable Development Goal, SDG）1的进展——即消除一切形式的贫困。它还为政策制定者提供数据支撑，以响应目标1.2的号召："根据各国定义，将各年龄段的男性、女性和儿童中处于一切形式贫困中的比例至少降低一半"。

本数据集中的每一行代表国家层面的汇总统计数据。数据最近一次在HDX上的更新时间为2026-03-04。地理范围：**多哥（TGO）**。

*本数据集已由[非洲电羊（Electric Sheep Africa）](https://huggingface.co/electricsheepafrica)整理为适配机器学习的Parquet格式。*

---

## 数据集特征

| | |
|---|---|
| **研究领域** | 公共卫生 |
| **观测单元** | 国家层面汇总数据 |
| **总数据行数** | 881 |
| **列数** | 10列（2列数值型，8列分类型，0列日期型） |
| **训练集拆分** | 704行 |
| **测试集拆分** | 176行 |
| **地理范围** | 多哥（TGO） |
| **发布方** | 联合国开发计划署人类发展报告办公室（HDRO） |
| **HDX最后更新时间** | 2026-03-04 |

---

## 变量说明

**地理类变量** — `country_code`（国家代码，取值为TGO）、`country_name`（国家名称，取值为多哥）、`index_id`（指数代码，取值为GDI、GII、HDI）、`index_name`（指数名称，取值为性别发展指数、性别不平等指数、人类发展指数）、`year`（年份，范围为1990.0–2023.0）。

**结果/测量变量** — `value`（数值，范围为0.18–3237.116）。

**标识符/元数据变量** — `indicator_id`（指标代码，取值为eys、pr_f、eys_f）、`indicator_name`（指标名称，取值为预期受教育年限（年）、议会席位女性占比（%）、女性预期受教育年限（年））、`esa_source`（数据来源，取值为HDX）、`esa_processed`（数据整理日期，取值为2026-04-06）。

---

## 快速上手

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-hdro-data-for-togo")
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 | 空值占比 | 取值范围/示例值 |
|---|---|---|---|
| `country_code` | 字符串型 | 0.0% | TGO |
| `country_name` | 字符串型 | 0.0% | 多哥 |
| `indicator_id` | 字符串型 | 0.0% | eys、pr_f、eys_f |
| `indicator_name` | 字符串型 | 0.0% | 预期受教育年限（年）、议会席位女性占比（%）、女性预期受教育年限（年） |
| `index_id` | 字符串型 | 0.0% | GDI、GII、HDI |
| `index_name` | 字符串型 | 0.0% | 性别发展指数、性别不平等指数、人类发展指数 |
| `value` | float64型 | 0.0% | 0.18 – 3237.116（均值为228.8846） |
| `year` | int64型 | 0.0% | 1990.0 – 2023.0（均值为2008.4949） |
| `esa_source` | 字符串型 | 0.0% | HDX |
| `esa_processed` | 字符串型 | 0.0% | 2026-04-06 |

---

## 数值统计摘要

| 列名 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|---|
| `value` | 0.18 | 3237.116 | 228.8846 | 14.536 |
| `year` | 1990.0 | 2023.0 | 2008.4949 | 2010.0 |

---

## 数据整理流程

原始数据通过CKAN API从HDX下载，并转换为Parquet格式。列名统一转换为小写并采用蛇形命名法（snake_case）进行标准化。将常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）统一替换为`NaN`。本数据集采用固定随机种子（42）按80/20的比例划分为训练集与测试集，并保存为Snappy压缩的Parquet格式。

---

## 局限性说明

- 数据来源于联合国开发计划署人类发展报告办公室（HDRO），未经非洲电羊（ESA）独立验证。
- 自动化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。
- 请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/hdx-data-for-togo)获取发布方提供的方法论说明与免责条款。

---

## 引用格式

bibtex
@dataset{hdx_africa_hdro_data_for_togo,
title = {多哥 - 人类发展指标},
author = {联合国开发计划署人类发展报告办公室（HDRO)},
year = {2026},
url = {https://data.humdata.org/dataset/hdx-data-for-togo},
note = {由非洲电羊（https://huggingface.co/electricsheepafrica）重新打包以适配机器学习场景}
}

---

*[非洲电羊（Electric Sheep Africa）](https://huggingface.co/electricsheepafrica) — 非洲的机器学习数据集基础设施。尼日利亚拉各斯。*