electricsheepafrica/africa-world-bank-infrastructure-indicators-for-angola

--- 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 - facilities-infrastructure - indicators - ago pretty_name: "Angola - Infrastructure" dataset_info: splits: - name: train num_examples: 1044 - name: test num_examples: 261 --- # Angola - Infrastructure **Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-infrastructure-indicators-for-angola) · **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-angola) on HDX. Infrastructure helps determine the success of manufacturing and agricultural activities. Investments in water, sanitation, energy, housing, and transport also improve lives and help reduce poverty. And new information and communication technologies promote growth, improve delivery of health and other services, expand the reach of education, and support social and cultural advances. Data here are compiled from such sources as the International Road Federation, Containerisation International, the International Civil Aviation Organization, the International Energy Association, and the International Telecommunications Union. Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: **AGO**. *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)** | 1,305 | | **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) | | **Train split** | 1,044 rows | | **Test split** | 261 rows | | **Geographic scope** | AGO | | **Publisher** | World Bank Group | | **HDX last updated** | 2026-03-27 | --- ## Variables **Geographic** — `country_name` (Angola), `country_iso3` (AGO), `year` (range 1960.0–2024.0). **Outcome / Measurement** — `value` (range 0.0–54082682000.0). **Identifier / Metadata** — `indicator_name` (Fixed telephone subscriptions, Fixed telephone subscriptions (per 100 people), Renewable internal freshwater resources per capita (cubic meters)), `indicator_code` (IT.MLT.MAIN, IT.MLT.MAIN.P2, ER.H2O.INTR.PC), `esa_source` (HDX), `esa_processed` (2026-04-17). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-world-bank-infrastructure-indicators-for-angola") 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% | Angola | | `country_iso3` | object | 0.0% | AGO | | `year` | int64 | 0.0% | 1960.0 – 2024.0 (mean 2001.3732) | | `indicator_name` | object | 0.0% | Fixed telephone subscriptions, Fixed telephone subscriptions (per 100 people), Renewable internal freshwater resources per capita (cubic meters) | | `indicator_code` | object | 0.0% | IT.MLT.MAIN, IT.MLT.MAIN.P2, ER.H2O.INTR.PC | | `value` | float64 | 0.0% | 0.0 – 54082682000.0 (mean 55089221.9915) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-17 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `year` | 1960.0 | 2024.0 | 2001.3732 | 2003.0 | | `value` | 0.0 | 54082682000.0 | 55089221.9915 | 38.0 | --- ## 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-infrastructure-indicators-for-angola) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_world_bank_infrastructure_indicators_for_angola, title = {Angola - Infrastructure}, author = {World Bank Group}, year = {2026}, url = {https://data.humdata.org/dataset/world-bank-infrastructure-indicators-for-angola}, 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: - 现有公开资源采集 language: - 英语 license: cc-by-4.0 multilinguality: - 单语言 size_categories: - 1000<n<10000 source_datasets: - 原始数据集 task_categories: - 表格分类 task_ids: [] tags: - 非洲 - 人道主义 - HDX - Electric Sheep Africa - 设施与基础设施 - 指标 - AGO pretty_name: "安哥拉——基础设施" dataset_info: splits: - name: train num_examples: 1044 - name: test num_examples: 261 # 安哥拉——基础设施 **发布方：** 世界银行集团 · **数据源：** [HDX](https://data.humdata.org/dataset/world-bank-infrastructure-indicators-for-angola) · **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平台上另有一份[安哥拉综合国家数据集](https://data.humdata.org/dataset/world-bank-combined-indicators-for-angola)可供获取。 基础设施水平是制造业与农业活动成败的关键影响因素。对供水、环卫、能源、住房与交通领域的投资不仅能改善民众生活，更有助于减缓贫困。而新兴信息与通信技术则可推动经济增长、优化医疗与其他公共服务的交付效率、拓展教育覆盖范围，并助力社会与文化进步。本数据集整合了来自国际道路联合会、国际集装箱运输期刊、国际民用航空组织、国际能源署以及国际电信联盟的各类数据。 本数据集的每一行均代表国家级汇总统计数据。HDX平台上的最新更新时间为2026-03-27。地理覆盖范围：**AGO（安哥拉）**。 *本数据集已由[Electric Sheep Africa（非洲电羊团队）](https://huggingface.co/electricsheepafrica)整理为可供机器学习直接使用的Parquet格式文件。* --- ## 数据集特征 | | | |---|---| | **领域** | 公共卫生 | | **观测单元** | 国家级汇总统计数据 | | **总行数** | 1,305 | | **列数** | 8（2个数值型列、6个分类型列、0个日期时间型列） | | **训练集划分** | 1,044条数据 | | **测试集划分** | 261条数据 | | **地理覆盖范围** | AGO（安哥拉） | | **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：安哥拉）、`country_iso3`（国家ISO3代码：AGO）、`year`（年份范围：1960.0–2024.0）。 **结果/测量类变量** — `value`（指标数值范围：0.0–54082682000.0）。 **标识符/元数据类变量** — `indicator_name`（指标名称：固定电话订阅量、固定电话订阅量（每百人）、可再生内陆淡水资源人均占有量（立方米））、`indicator_code`（指标代码：IT.MLT.MAIN、IT.MLT.MAIN.P2、ER.H2O.INTR.PC）、`esa_source`（数据来源：HDX）、`esa_processed`（数据处理时间：2026-04-17）。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-world-bank-infrastructure-indicators-for-angola") 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_name` | `object` | 0.0% | 安哥拉 | | `country_iso3` | `object` | 0.0% | AGO | | `year` | `int64` | 0.0% | 1960.0 – 2024.0（均值：2001.3732） | | `indicator_name` | `object` | 0.0% | 固定电话订阅量、固定电话订阅量（每百人）、可再生内陆淡水资源人均占有量（立方米） | | `indicator_code` | `object` | 0.0% | IT.MLT.MAIN、IT.MLT.MAIN.P2、ER.H2O.INTR.PC | | `value` | `float64` | 0.0% | 0.0 – 54082682000.0（均值：55089221.9915） | | `esa_source` | `object` | 0.0% | HDX | | `esa_processed` | `object` | 0.0% | 2026-04-17 | --- ## 数值型变量统计 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `year` | 1960.0 | 2024.0 | 2001.3732 | 2003.0 | | `value` | 0.0 | 54082682000.0 | 55089221.9915 | 38.0 | --- ## 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，并以Snappy压缩格式保存为Parquet文件。 --- ## 数据集局限性 - 本数据集源自世界银行集团，未经过Electric Sheep Africa团队的独立验证。 - 自动化清洗流程无法修正原始数据收集阶段存在的错报值、定义不一致或采样偏差问题。 - 如需查看发布方提供的官方方法论说明与注意事项，请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/world-bank-infrastructure-indicators-for-angola)。 --- ## 引用格式 bibtex @dataset{hdx_africa_world_bank_infrastructure_indicators_for_angola, title = {Angola - Infrastructure}, author = {World Bank Group}, year = {2026}, url = {https://data.humdata.org/dataset/world-bank-infrastructure-indicators-for-angola}, note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)} } --- *[Electric Sheep Africa（非洲电羊团队）](https://huggingface.co/electricsheepafrica) — 非洲的机器学习数据集基础设施平台。尼日利亚拉各斯。*