electricsheepafrica/africa-unesco-data-for-uganda

--- 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 - demographics - education - indicators - socioeconomics - sustainable-development - sustainable-development-goals-sdg - uga pretty_name: "Uganda - Education Indicators" dataset_info: splits: - name: train num_examples: 4737 - name: test num_examples: 1184 --- # Uganda - Education Indicators **Publisher:** UNESCO · **Source:** [HDX](https://data.humdata.org/dataset/unesco-data-for-uganda) · **License:** `cc-by-igo` · **Updated:** 2026-03-03 --- ## Abstract Education indicators for Uganda. Contains data from the UNESCO Institute for Statistics [bulk data service](http://data.uis.unesco.org) covering the following categories: SDG 4 Global and Thematic (made 2026 February), Other Policy Relevant Indicators (made 2026 February), Demographic and Socio-economic (made 2026 February) Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-03. Geographic scope: **UGA**. *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)** | 5,922 | | **Columns** | 6 (2 numeric, 4 categorical, 0 datetime) | | **Train split** | 4,737 rows | | **Test split** | 1,184 rows | | **Geographic scope** | UGA | | **Publisher** | UNESCO | | **HDX last updated** | 2026-03-03 | --- ## Variables **Geographic** — `country_id` (UGA), `year` (range 1970.0–2025.0). **Outcome / Measurement** — `value` (range 0.0–6879382.0). **Identifier / Metadata** — `indicator_id` (CR.MOD.1.F, CR.MOD.1, CR.MOD.1.GPIA), `esa_source` (HDX), `esa_processed` (2026-04-05). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-unesco-data-for-uganda") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `indicator_id` | object | 0.0% | CR.MOD.1.F, CR.MOD.1, CR.MOD.1.GPIA | | `country_id` | object | 0.0% | UGA | | `year` | int64 | 0.0% | 1970.0 – 2025.0 (mean 2010.8316) | | `value` | float64 | 0.0% | 0.0 – 6879382.0 (mean 14718.4078) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-05 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `year` | 1970.0 | 2025.0 | 2010.8316 | 2012.0 | | `value` | 0.0 | 6879382.0 | 14718.4078 | 9.105 | --- ## 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) with >80% missing values were removed: `magnitude`, `qualifier`. 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 UNESCO 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/unesco-data-for-uganda) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_unesco_data_for_uganda, title = {Uganda - Education Indicators}, author = {UNESCO}, year = {2026}, url = {https://data.humdata.org/dataset/unesco-data-for-uganda}, 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: - 现有数据源提取 language: - 英语 license: cc-by-4.0 multilinguality: - 单语种 size_categories: - 1000 < 样本数 < 10000 source_datasets: - 原生数据集 task_categories: - 表格分类 - 表格回归 task_ids: [] tags: - 非洲 - 人道主义 - 人道主义数据交换（HDX） - 电羊非洲（Electric Sheep Africa） - 人口统计学 - 教育 - 指标 - 社会经济 - 可持续发展 - 可持续发展目标（SDG） - 乌干达（UGA） pretty_name: "乌干达——教育指标" dataset_info: splits: - name: 训练集 num_examples: 4737 - name: 测试集 num_examples: 1184 --- # 乌干达——教育指标数据集 **发布方**：联合国教科文组织（UNESCO） · **来源**：[人道主义数据交换（HDX）](https://data.humdata.org/dataset/unesco-data-for-uganda) · **许可协议**：`cc-by-igo` · **更新时间**：2026-03-03 --- ## 摘要 本数据集包含乌干达的教育指标数据。 数据源自联合国教科文组织统计研究所（UNESCO Institute for Statistics）的[批量数据服务](http://data.uis.unesco.org)，涵盖以下类别：可持续发展目标4（SDG 4）全球与主题类指标（2026年2月更新）、其他政策相关指标（2026年2月更新）、人口与社会经济类指标（2026年2月更新）。 数据集中每一行均代表国家级汇总数据。数据集最后于2026-03-03在HDX平台更新。地理覆盖范围：**UGA（乌干达）**。 *本数据集由[电羊非洲（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** | 教育 | | **观测单元** | 国家级汇总数据 | | **总样本行数** | 5922 | | **列数** | 6列（2个数值型、4个分类型、0个日期时间型） | | **训练集划分** | 4737行 | | **测试集划分** | 1184行 | | **地理覆盖范围** | UGA（乌干达） | | **发布方** | 联合国教科文组织（UNESCO） | | **HDX平台最后更新时间** | 2026-03-03 | --- ## 变量说明 **地理相关变量**：`country_id`（国家代码，取值为UGA）、`year`（年份，取值范围1970.0~2025.0）。 **结果/测量变量**：`value`（指标数值，取值范围0.0~6879382.0）。 **标识符/元数据变量**：`indicator_id`（指标代码，可选值为CR.MOD.1.F、CR.MOD.1、CR.MOD.1.GPIA）、`esa_source`（数据来源，取值为HDX）、`esa_processed`（数据处理时间，取值为2026-04-05）。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-unesco-data-for-uganda") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() --- ## 表结构 | 列名 | 数据类型 | 空值占比 | 取值范围/示例值 | |---|---|---|---| | `indicator_id` | 字符串型 | 0.0% | CR.MOD.1.F、CR.MOD.1、CR.MOD.1.GPIA | | `country_id` | 字符串型 | 0.0% | UGA | | `year` | 64位整型 | 0.0% | 1970.0~2025.0（均值2010.8316） | | `value` | 64位浮点型 | 0.0% | 0.0~6879382.0（均值14718.4078） | | `esa_source` | 字符串型 | 0.0% | HDX | | `esa_processed` | 字符串型 | 0.0% | 2026-04-05 | --- ## 数值统计摘要 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `year` | 1970.0 | 2025.0 | 2010.8316 | 2012.0 | | `value` | 0.0 | 6879382.0 | 14718.4078 | 9.105 | --- ## 数据整理流程 原始数据通过CKAN应用程序编程接口（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。常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）被统一替换为`NaN`。移除了2个缺失值占比超过80%的列：`magnitude`与`qualifier`。数据集以80:20的比例划分为训练集与测试集，使用固定随机种子（42）进行划分，并保存为采用Snappy压缩的Parquet格式文件。 --- ## 数据局限性 - 数据源自联合国教科文组织，未经过电羊非洲（ESA）的独立验证。 - 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。 - 请查阅[HDX平台原始数据集页面](https://data.humdata.org/dataset/unesco-data-for-uganda)以获取发布方提供的方法论说明与注意事项。 --- ## 引用格式 bibtex @dataset{hdx_africa_unesco_data_for_uganda, title = {Uganda - Education Indicators}, author = {UNESCO}, year = {2026}, url = {https://data.humdata.org/dataset/unesco-data-for-uganda}, 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，尼日利亚拉各斯。*