electricsheepafrica/africa-unesco-data-for-gambia
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--- 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 - gmb pretty_name: "Gambia - Education Indicators" dataset_info: splits: - name: train num_examples: 6244 - name: test num_examples: 1561 --- # Gambia - Education Indicators **Publisher:** UNESCO · **Source:** [HDX](https://data.humdata.org/dataset/unesco-data-for-gambia) · **License:** `cc-by-igo` · **Updated:** 2026-03-02 --- ## Abstract Education indicators for Gambia. 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 2026-03-02. Geographic scope: **GMB**. *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)** | 7,805 | | **Columns** | 6 (2 numeric, 4 categorical, 0 datetime) | | **Train split** | 6,244 rows | | **Test split** | 1,561 rows | | **Geographic scope** | GMB | | **Publisher** | UNESCO | | **HDX last updated** | 2026-03-02 | --- ## Variables **Geographic** — `country_id` (GMB), `year` (range 1971.0–2025.0). **Outcome / Measurement** — `value` (range 0.0–8906923.0). **Identifier / Metadata** — `indicator_id` (AIR.1.GLAST, AIR.1.GLAST.F, AIR.1.GLAST.GPIA), `esa_source` (HDX), `esa_processed` (2026-04-04). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-unesco-data-for-gambia") 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% | AIR.1.GLAST, AIR.1.GLAST.F, AIR.1.GLAST.GPIA | | `country_id` | object | 0.0% | GMB | | `year` | int64 | 0.0% | 1971.0 – 2025.0 (mean 2012.8054) | | `value` | float64 | 0.0% | 0.0 – 8906923.0 (mean 5240.9167) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-04 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `year` | 1971.0 | 2025.0 | 2012.8054 | 2015.0 | | `value` | 0.0 | 8906923.0 | 5240.9167 | 22.4288 | --- ## 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-gambia) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_unesco_data_for_gambia, title = {Gambia - Education Indicators}, author = {UNESCO}, year = {2026}, url = {https://data.humdata.org/dataset/unesco-data-for-gambia}, 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: 标注创建者 - 无标注 language_creators: 语言采集方式 - 现有公开数据 language: 语言 - 英语 license: 许可协议 - CC BY 4.0 multilinguality: 多语言属性 - 单语言 size_categories: 样本量区间 - 1000 < 样本数 < 10000 source_datasets: 源数据集 - 原生数据集 task_categories: 任务类别 - 表格分类 - 表格回归 task_ids: 任务子类别 - 无 tags: 标签 - 非洲 - 人道主义 - HDX - Electric Sheep Africa - 人口统计学 - 教育 - 指标 - 社会经济学 - 可持续发展 - 可持续发展目标 (Sustainable Development Goals, SDG) - GMB pretty_name: 冈比亚——教育指标 dataset_info: splits: - name: 训练集 num_examples: 6244 - name: 测试集 num_examples: 1561 --- # 冈比亚——教育指标 **发布方**：联合国教科文组织 (UNESCO) · **来源**：[人道主义数据交换平台 (HDX)](https://data.humdata.org/dataset/unesco-data-for-gambia) · **许可协议**：`cc-by-igo` · **更新时间**：2026-03-02 --- ## 摘要 本数据集涵盖冈比亚的教育指标数据。数据取自联合国教科文组织统计研究所的批量数据服务，涵盖以下类别：2026年2月生成的可持续发展目标4（SDG 4）全球与专题指标、2026年2月生成的其他政策相关指标、2026年2月生成的人口与社会经济指标。 数据集中每一行均代表国家层面的汇总统计值。该数据集在HDX平台的最后更新时间为2026年3月2日。地理覆盖范围：**GMB（冈比亚国家代码）**。 *本数据集经[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** | 教育 | | **观测单元** | 国家层面汇总数据 | | **总行数** | 7805 | | **列数** | 6（2个数值型、4个分类型、0个日期时间型） | | **训练集划分** | 6244行 | | **测试集划分** | 1561行 | | **地理覆盖范围** | GMB | | **发布方** | 联合国教科文组织 (UNESCO) | | **HDX最后更新时间** | 2026-03-02 | --- ## 变量说明 **地理类变量**：`country_id`（GMB）、`year`（取值范围1971.0~2025.0）。 **结果/测量类变量**：`value`（取值范围0.0~8906923.0）。 **标识符/元数据类变量**：`indicator_id`（AIR.1.GLAST、AIR.1.GLAST.F、AIR.1.GLAST.GPIA）、`esa_source`（HDX）、`esa_processed`（2026-04-04）。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-unesco-data-for-gambia") 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 | |---|---|---|---| | `indicator_id` | 对象类型（pandas的object类型） | 0.0% | AIR.1.GLAST、AIR.1.GLAST.F、AIR.1.GLAST.GPIA | | `country_id` | 对象类型 | 0.0% | GMB | | `year` | 整型（int64） | 0.0% | 1971.0 – 2025.0（均值2012.8054） | | `value` | 浮点型（float64） | 0.0% | 0.0 – 8906923.0（均值5240.9167） | | `esa_source` | 对象类型 | 0.0% | HDX | | `esa_processed` | 对象类型 | 0.0% | 2026-04-04 | --- ## 数值型变量统计摘要 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `year` | 1971.0 | 2025.0 | 2012.8054 | 2015.0 | | `value` | 0.0 | 8906923.0 | 5240.9167 | 22.4288 | --- ## 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）标准化。常见缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）被统一替换为`NaN`。删除了2个缺失值占比超过80%的列：`magnitude`、`qualifier`。本数据集采用固定随机种子（42）按照80/20的比例划分为训练集与测试集，并以Snappy压缩格式的Parquet文件存储。 --- ## 数据集局限性 - 数据源自联合国教科文组织，未经过Electric Sheep Africa的独立验证。 - 自动化清洗流程无法修正原始数据采集过程中存在的错报值、定义不一致或抽样偏差问题。 - 如需查看发布方的方法说明与免责声明，请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/unesco-data-for-gambia)。 --- ## 引用格式 bibtex @dataset{hdx_africa_unesco_data_for_gambia, title = {Gambia - Education Indicators}, author = {UNESCO}, year = {2026}, url = {https://data.humdata.org/dataset/unesco-data-for-gambia}, note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)} } --- *[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — 非洲的机器学习数据集基础设施。尼日利亚拉各斯。*
