electricsheepafrica/africa-world-bank-education-indicators-for-south-sudan

--- 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 - tabular-regression task_ids: [] tags: - africa - humanitarian - hdx - electric-sheep-africa - education - indicators - ssd pretty_name: "South Sudan - Education" dataset_info: splits: - name: train num_examples: 1492 - name: test num_examples: 373 --- # South Sudan - Education **Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-education-indicators-for-south-sudan) · **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-south-sudan) on HDX. Education is one of the most powerful instruments for reducing poverty and inequality and lays a foundation for sustained economic growth. The World Bank compiles data on education inputs, participation, efficiency, and outcomes. Data on education are compiled by the United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics from official responses to surveys and from reports provided by education authorities in each country. Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: **SSD**. *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).* --- ## Dataset Characteristics | | | |---|---| | **Domain** | Education | | **Unit of observation** | Country-level aggregates | | **Rows (total)** | 1,865 | | **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) | | **Train split** | 1,492 rows | | **Test split** | 373 rows | | **Geographic scope** | SSD | | **Publisher** | World Bank Group | | **HDX last updated** | 2026-03-27 | --- ## Variables **Geographic** — `country_name` (South Sudan), `country_iso3` (SSD), `year` (range 1960.0–2024.0). **Outcome / Measurement** — `value` (range 0.0–5070344.0). **Identifier / Metadata** — `indicator_name` (Population ages 15-64 (% of total population), Population ages 0-14 (% of total population), Number of under-five deaths, male), `indicator_code` (SP.POP.1564.TO.ZS, SP.POP.0014.TO.ZS, SH.DTH.MORT.MA), `esa_source` (HDX), `esa_processed` (2026-04-10). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-world-bank-education-indicators-for-south-sudan") 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% | South Sudan | | `country_iso3` | object | 0.0% | SSD | | `year` | int64 | 0.0% | 1960.0 – 2024.0 (mean 2006.6123) | | `indicator_name` | object | 0.0% | Population ages 15-64 (% of total population), Population ages 0-14 (% of total population), Number of under-five deaths, male | | `indicator_code` | object | 0.0% | SP.POP.1564.TO.ZS, SP.POP.0014.TO.ZS, SH.DTH.MORT.MA | | `value` | float64 | 0.0% | 0.0 – 5070344.0 (mean 196338.2057) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-10 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `year` | 1960.0 | 2024.0 | 2006.6123 | 2011.0 | | `value` | 0.0 | 5070344.0 | 196338.2057 | 113.7 | --- ## 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-education-indicators-for-south-sudan) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_world_bank_education_indicators_for_south_sudan, title = {South Sudan - Education}, author = {World Bank Group}, year = {2026}, url = {https://data.humdata.org/dataset/world-bank-education-indicators-for-south-sudan}, 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: - 公开采集 language: - 英语 license: CC-BY-4.0 multilinguality: - 单语言 size_categories: - 1000 < 样本数 < 10000 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 - 教育 - 指标 - SSD pretty_name: "南苏丹——教育" dataset_info: splits: - name: train num_examples: 1492 - name: test num_examples: 373 # 南苏丹——教育 **发布方：** 世界银行集团（World Bank Group） · **来源：** [人道主义数据交换平台（HDX）](https://data.humdata.org/dataset/world-bank-education-indicators-for-south-sudan) · **授权协议：** `CC-BY` · **更新时间：** 2026-03-27 --- ## 摘要 本数据集的数据源自世界银行集团（World Bank Group）的[数据门户](http://data.worldbank.org/)，同时HDX平台上还提供了一份[整合型国家数据集](https://data.humdata.org/dataset/world-bank-combined-indicators-for-south-sudan)。 教育是减少贫困与不平等最为有效的手段之一，同时也是可持续经济增长的根基。世界银行集团会收集教育投入、参与度、教育效率与教育成果相关数据。教育领域数据由联合国教育、科学及文化组织（UNESCO）统计研究所通过官方调研反馈以及各国教育主管部门提交的报告汇总整理而来。 本数据集的每一行均代表国家级汇总数据。本数据集在HDX平台的最后更新时间为2026-03-27，地理覆盖范围：**南苏丹（SSD）**。 *本数据集已由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为适配机器学习的Parquet列式存储格式（Parquet）。* --- ## 数据集特征 | | | |---|---| | **领域** | 教育 | | **观测单元** | 国家级汇总数据 | | **总样本行数** | 1865 | | **列数** | 8列（2个数值型、6个分类型、0个日期时间型） | | **训练集划分** | 1492行 | | **测试集划分** | 373行 | | **地理覆盖范围** | 南苏丹（SSD） | | **发布方** | 世界银行集团（World Bank Group） | | **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：南苏丹）、`country_iso3`（国家ISO3代码：SSD）、`year`（年份范围：1960.0至2024.0）。 **结果/测量类变量** — `value`（指标数值，范围：0.0至5070344.0）。 **标识符/元数据类变量** — `indicator_name`（指标名称，包含：15-64岁人口占总人口比例、0-14岁人口占总人口比例、男性五岁以下死亡人数）、`indicator_code`（指标代码，包含：SP.POP.1564.TO.ZS、SP.POP.0014.TO.ZS、SH.DTH.MORT.MA）、`esa_source`（数据来源：HDX）、`esa_processed`（数据处理时间：2026-04-10）。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-world-bank-education-indicators-for-south-sudan") 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` | 对象型（object） | 0.0% | 南苏丹 | | `country_iso3` | 对象型（object） | 0.0% | SSD | | `year` | 64位整型（int64） | 0.0% | 1960.0 至 2024.0（均值：2006.6123） | | `indicator_name` | 对象型（object） | 0.0% | 15-64岁人口占总人口比例、0-14岁人口占总人口比例、男性五岁以下死亡人数 | | `indicator_code` | 对象型（object） | 0.0% | SP.POP.1564.TO.ZS、SP.POP.0014.TO.ZS、SH.DTH.MORT.MA | | `value` | 64位浮点型（float64） | 0.0% | 0.0 至 5070344.0（均值：196338.2057） | | `esa_source` | 对象型（object） | 0.0% | HDX | | `esa_processed` | 对象型（object） | 0.0% | 2026-04-10 | --- ## 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 | 2006.6123 | 2011.0 | | `value` | 0.0 | 5070344.0 | 196338.2057 | 113.7 | --- ## 数据整理流程 原始数据通过综合知识归档网络（CKAN）API从HDX平台下载，并转换为Parquet列式存储格式（Parquet）。所有列名均转为小写并标准化为蛇形命名法（snake_case）。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，并以Snappy压缩的Parquet格式存储。 --- ## 数据集局限性 - 本数据集的数据源自世界银行集团，并未由Electric Sheep Africa（ESA）进行独立验证。 - 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。 - 如需查看发布方提供的方法说明与注意事项，请参阅[HDX平台原始数据集页面](https://data.humdata.org/dataset/world-bank-education-indicators-for-south-sudan)。 --- ## 引用格式 bibtex @dataset{hdx_africa_world_bank_education_indicators_for_south_sudan, title = {South Sudan - Education}, author = {World Bank Group}, year = {2026}, url = {https://data.humdata.org/dataset/world-bank-education-indicators-for-south-sudan}, note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)} } --- *[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — 非洲地区机器学习数据集基础设施，尼日利亚拉各斯。*