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

--- 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 task_ids: [] tags: - africa - humanitarian - hdx - electric-sheep-africa - facilities-infrastructure - hxl - indicators - som pretty_name: "Somalia - Infrastructure" dataset_info: splits: - name: train num_examples: 752 - name: test num_examples: 188 --- # Somalia - Infrastructure **Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-infrastructure-indicators-for-somalia) · **License:** `cc-by` · **Updated:** 2025-08-28 --- ## 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-somalia) 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 2025-08-28. Geographic scope: **SOM**. *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)** | 940 | | **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) | | **Train split** | 752 rows | | **Test split** | 188 rows | | **Geographic scope** | SOM | | **Publisher** | World Bank Group | | **HDX last updated** | 2025-08-28 | --- ## Variables **Geographic** — `country_name` (Somalia, #country+name), `country_iso3` (SOM, #country+code), `year` (range 1960.0–2024.0). **Outcome / Measurement** — `value` (range 0.0–442000000.0). **Identifier / Metadata** — `indicator_name` (Renewable internal freshwater resources, total (billion cubic meters), Renewable internal freshwater resources per capita (cubic meters), Fixed telephone subscriptions), `indicator_code` (ER.H2O.INTR.K3, ER.H2O.INTR.PC, IT.MLT.MAIN), `esa_source` (HDX), `esa_processed` (2026-04-08). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-world-bank-infrastructure-indicators-for-somalia") 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% | Somalia, #country+name | | `country_iso3` | object | 0.0% | SOM, #country+code | | `year` | float64 | 0.1% | 1960.0 – 2024.0 (mean 1999.8371) | | `indicator_name` | object | 0.0% | Renewable internal freshwater resources, total (billion cubic meters), Renewable internal freshwater resources per capita (cubic meters), Fixed telephone subscriptions | | `indicator_code` | object | 0.0% | ER.H2O.INTR.K3, ER.H2O.INTR.PC, IT.MLT.MAIN | | `value` | float64 | 0.1% | 0.0 – 442000000.0 (mean 1451173.7936) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-08 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `year` | 1960.0 | 2024.0 | 1999.8371 | 2002.0 | | `value` | 0.0 | 442000000.0 | 1451173.7936 | 6.0 | --- ## 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`. 2 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). 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-somalia) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_world_bank_infrastructure_indicators_for_somalia, title = {Somalia - Infrastructure}, author = {World Bank Group}, year = {2025}, url = {https://data.humdata.org/dataset/world-bank-infrastructure-indicators-for-somalia}, 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: - 现有采集 language: - 英语 license: CC BY 4.0 multilinguality: - 单语言 size_categories: - 样本数少于1000 source_datasets: - 原始数据集 task_categories: - 表格分类（tabular-classification） task_ids: [] tags: - 非洲 - 人道主义 - HDX（Humanitarian Data Exchange） - 电羊非洲（Electric Sheep Africa） - 设施与基础设施 - HXL（Humanitarian Exchange Language） - 指标 - SOM（索马里ISO 3代码） pretty_name: "索马里——基础设施" dataset_info: splits: - name: train num_examples: 752 - name: test num_examples: 188 --- # 索马里——基础设施 **发布方**：世界银行集团 · **数据源**：[HDX（Humanitarian Data Exchange）](https://data.humdata.org/dataset/world-bank-infrastructure-indicators-for-somalia) · **许可证**：`cc-by` · **更新时间**：2025-08-28 --- ## 摘要 本数据集包含来自世界银行[数据门户](http://data.worldbank.org/)的相关数据。HDX平台上另有一份[索马里综合国家数据集](https://data.humdata.org/dataset/world-bank-combined-indicators-for-somalia)。 基础设施水平是制造业与农业活动成败的关键影响因素。在供水、卫生、能源、住房与交通领域的投资，不仅能改善民众生活，更有助于减贫。而新兴信息与通信技术则可推动经济增长、优化医疗及其他公共服务的供给、扩大教育覆盖范围，并助力社会与文化进步。本数据集的数据来源于国际道路联合会、国际集装箱化协会、国际民用航空组织、国际能源署以及国际电信联盟等机构。 本数据集的每一行均代表国家级汇总统计数据。该数据集在HDX平台的最后更新时间为2025-08-28。地理覆盖范围：**SOM（索马里ISO 3代码）**。 *本数据集已由[电羊非洲（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 | | **总行数** | 940 | | **列数** | 8（其中2列为数值型，6列为分类型，0个日期时间型列） | | **训练集划分** | 752行 | | **测试集划分** | 188行 | | **地理覆盖范围** | SOM | | **发布方** | 世界银行集团 | | **HDX最后更新时间** | 2025-08-28 | --- ## 变量说明 **地理类变量** — `country_name`（索马里，#country+name）、`country_iso3`（SOM，#country+code）、`year`（取值范围1960.0–2024.0）。 **结果/测量类变量** — `value`（取值范围0.0–442000000.0）。 **标识符/元数据类变量** — `indicator_name`（可再生内陆淡水资源总量（十亿立方米）、人均可再生内陆淡水资源量（立方米）、固定电话订阅量）、`indicator_code`（ER.H2O.INTR.K3、ER.H2O.INTR.PC、IT.MLT.MAIN）、`esa_source`（HDX）、`esa_processed`（2026-04-08）。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-world-bank-infrastructure-indicators-for-somalia") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() --- ## 数据模式 | 列名 | 数据类型 | 空值占比 | 取值范围/示例值 | |---|---|---|---| | `country_name` | 字符串型（object） | 0.0% | 索马里，#country+name | | `country_iso3` | 字符串型（object） | 0.0% | SOM，#country+code | | `year` | 浮点型（float64） | 0.1% | 1960.0 – 2024.0（均值1999.8371） | | `indicator_name` | 字符串型（object） | 0.0% | 可再生内陆淡水资源总量（十亿立方米）、人均可再生内陆淡水资源量（立方米）、固定电话订阅量 | | `indicator_code` | 字符串型（object） | 0.0% | ER.H2O.INTR.K3、ER.H2O.INTR.PC、IT.MLT.MAIN | | `value` | 浮点型（float64） | 0.1% | 0.0 – 442000000.0（均值1451173.7936） | | `esa_source` | 字符串型（object） | 0.0% | HDX | | `esa_processed` | 字符串型（object） | 0.0% | 2026-04-08 | --- ## 数值型变量统计摘要 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `year` | 1960.0 | 2024.0 | 1999.8371 | 2002.0 | | `value` | 0.0 | 442000000.0 | 1451173.7936 | 6.0 | --- ## 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。对列名进行了小写化处理，并统一转换为蛇形命名法。将常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）统一替换为`NaN`。根据解析成功率（阈值>85%），将2列从字符串类型转换为数值型或日期时间型。本数据集以固定随机种子（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。 --- ## 局限性说明 - 本数据集的数据源自世界银行集团，并未经过电羊非洲（Electric Sheep Africa）的独立验证。 - 自动化清洗流程无法修正原始数据收集中存在的错报、定义不一致或抽样偏差问题。 - 如需查看发布方提供的方法学说明与注意事项，请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/world-bank-infrastructure-indicators-for-somalia)。 --- ## 引用格式 bibtex @dataset{hdx_africa_world_bank_infrastructure_indicators_for_somalia, title = {Somalia - Infrastructure}, author = {World Bank Group}, year = {2025}, url = {https://data.humdata.org/dataset/world-bank-infrastructure-indicators-for-somalia}, note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)} } --- *[电羊非洲（Electric Sheep Africa）](https://huggingface.co/electricsheepafrica) — 非洲机器学习数据集基础设施提供商，尼日利亚拉各斯。*