electricsheepafrica/africa-hdro-data-for-cameroon
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--- 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 - cmr pretty_name: "Cameroon - Human Development Indicators" dataset_info: splits: - name: train num_examples: 750 - name: test num_examples: 187 --- # Cameroon - Human Development Indicators **Publisher:** UNDP Human Development Reports Office (HDRO) · **Source:** [HDX](https://data.humdata.org/dataset/hdro-data-for-cameroon) · **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: **CMR**. *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)** | 938 | | **Columns** | 10 (2 numeric, 8 categorical, 0 datetime) | | **Train split** | 750 rows | | **Test split** | 187 rows | | **Geographic scope** | CMR | | **Publisher** | UNDP Human Development Reports Office (HDRO) | | **HDX last updated** | 2026-03-04 | --- ## Variables **Geographic** — `country_code` (CMR), `country_name` (Cameroon), `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 0.224–5870.496). **Identifier / Metadata** — `indicator_id` (eys, pop_total, mys_f), `indicator_name` (Expected Years of Schooling (years), Population, total (millions), Mean Years of Schooling, female (years)), `esa_source` (HDX), `esa_processed` (2026-04-08). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-hdro-data-for-cameroon") 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% | CMR | | `country_name` | object | 0.0% | Cameroon | | `indicator_id` | object | 0.0% | eys, pop_total, mys_f | | `indicator_name` | object | 0.0% | Expected Years of Schooling (years), Population, total (millions), Mean 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% | 0.224 – 5870.496 (mean 452.5281) | | `year` | int64 | 0.0% | 1990.0 – 2023.0 (mean 2007.9286) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-08 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `value` | 0.224 | 5870.496 | 452.5281 | 27.143 | | `year` | 1990.0 | 2023.0 | 2007.9286 | 2009.0 | --- ## 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-cameroon) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_hdro_data_for_cameroon, title = {Cameroon - Human Development Indicators}, author = {UNDP Human Development Reports Office (HDRO)}, year = {2026}, url = {https://data.humdata.org/dataset/hdro-data-for-cameroon}, 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: - 公开获取 language: - 英语 license: - CC BY 4.0 multilinguality: - 单语言 size_categories: - 样本量<1000 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 - HDX - Electric Sheep Africa - 人口统计 - 发展 - 教育 - 性别 - 健康 - 指标 - 社会经济 - CMR（喀麦隆） pretty_name: "喀麦隆——人类发展指标" dataset_info: splits: - name: train num_examples: 750 - name: test num_examples: 187 # 喀麦隆——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） · **数据源**：[HDX（人类数据交换平台）]("https://data.humdata.org/dataset/hdro-data-for-cameroon") · **许可证**：`CC-BY-IGO` · **更新时间**：2026-03-04 --- ## 摘要 人类发展报告旨在推动全球、区域及国家层面围绕与人类发展相关的议题开展贴合政策需求的讨论。因此，报告中的数据需满足最高标准的数据质量、一致性、国际可比性与透明度要求。人类发展报告办公室（HDRO）完全遵循国际统计活动相关准则。 人类发展指数（Human Development Index, HDI）的设立旨在强调：人民及其能力应成为评估国家发展的最终标准，而非仅以经济增长为依据。人类发展指数还可用于审视国家政策选择，探讨为何两国人均国民总收入（GNI）水平相当，最终的人类发展结果却存在差异。这类对比能够引发关于政府政策优先级的讨论。 人类发展指数（HDI）是对人类发展三大核心维度平均成就的综合衡量：健康长寿的生活、获取知识的能力以及体面的生活水平。该指数是三大维度标准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、国家及国家以下层级的贫困人口规模，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（UNDP）与牛津大学牛津贫困与人类发展倡议（OPHI）联合开发，2019年版全球MPI覆盖101个国家，惠及全球76%的人口。 多维贫困指数能够全面且深入地展现全球各维度的贫困状况，并追踪可持续发展目标1（SDG 1）——消除一切形式的贫困——的进展情况。同时，该指数可为政策制定者提供数据支持，以响应目标1.2的号召：“根据各国定义，将各年龄层男女儿童在各类贫困维度中的占比至少降低一半”。 本数据集的每一行均代表国家层面的汇总数据。该数据最后一次在HDX平台更新的时间为2026-03-04。地理覆盖范围：**CMR（喀麦隆）**。 *由[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 | | | |---|---| | **领域** | 公共卫生 | | **观测单元** | 国家层面汇总数据 | | **总样本行数** | 938 | | **列数** | 10列（2列数值型、8列分类型、0列日期型） | | **训练集样本量** | 750行 | | **测试集样本量** | 187行 | | **地理覆盖范围** | CMR（喀麦隆） | | **发布方** | UNDP人类发展报告办公室（HDRO） | | **HDX平台最后更新时间** | 2026-03-04 | --- ## 变量 **地理类变量** — `country_code`（国家代码，值为CMR）、`country_name`（国家名称，值为喀麦隆）、`index_id`（指数代码，取值为GDI、GII、HDI）、`index_name`（指数名称，对应性别发展指数（Gender Development Index, GDI）、性别不平等指数（Gender Inequality Index, GII）、人类发展指数（Human Development Index, HDI））、`year`（年份，范围为1990.0–2023.0）。 **结果/测量变量** — `value`（指标数值，范围为0.224–5870.496）。 **标识符/元数据变量** — `indicator_id`（指标代码，取值为eys、pop_total、mys_f）、`indicator_name`（指标名称，对应预期受教育年限（年）、总人口（百万）、女性平均受教育年限（年））、`esa_source`（数据来源，值为HDX）、`esa_processed`（数据整理时间，值为2026-04-08）。 --- ## 快速入门 以下为加载该数据集的示例代码： python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-hdro-data-for-cameroon") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() --- ## 数据Schema | 列名 | 数据类型 | 空值占比 | 取值范围/示例值 | |---|---|---|---| | `country_code` | 对象型（object） | 0.0% | CMR | | `country_name` | 对象型 | 0.0% | 喀麦隆 | | `indicator_id` | 对象型 | 0.0% | eys、pop_total、mys_f | | `indicator_name` | 对象型 | 0.0% | 预期受教育年限（年）、总人口（百万）、女性平均受教育年限（年） | | `index_id` | 对象型 | 0.0% | GDI、GII、HDI | | `index_name` | 对象型 | 0.0% | 性别发展指数、性别不平等指数、人类发展指数 | | `value` | float64 | 0.0% | 0.224 – 5870.496（均值为452.5281） | | `year` | int64 | 0.0% | 1990.0 – 2023.0（均值为2007.9286） | | `esa_source` | 对象型 | 0.0% | HDX | | `esa_processed` | 对象型 | 0.0% | 2026-04-08 | --- ## 数值统计摘要 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `value` | 0.224 | 5870.496 | 452.5281 | 27.143 | | `year` | 1990.0 | 2023.0 | 2007.9286 | 2009.0 | --- ## 数据整理 原始数据通过CKAN API从HDX平台下载，并转换为Parquet格式。列名统一转换为小写并标准化为蛇形命名法。常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）被统一替换为`NaN`。本数据集以固定随机种子（42）按照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 1. 本数据集源自联合国开发计划署人类发展报告办公室（HDRO），并未经Electric Sheep Africa（ESA）独立验证。 2. 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。 3. 如需了解发布方的方法论说明与免责条款，请参阅[HDX平台原始数据集页面]("https://data.humdata.org/dataset/hdro-data-for-cameroon")。 --- ## 引用格式 bibtex @dataset{hdx_africa_hdro_data_for_cameroon, title = {Cameroon - Human Development Indicators}, author = {UNDP Human Development Reports Office (HDRO)}, year = {2026}, url = {https://data.humdata.org/dataset/hdro-data-for-cameroon}, 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。*
