electricsheepafrica/africa-mozambique-school-data

--- annotations_creators: - no-annotation language_creators: - found language: - en license: cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - other task_ids: [] tags: - africa - humanitarian - hdx - electric-sheep-africa - education - education-facilities-schools - geodata - moz pretty_name: "Mozambique: Schools" dataset_info: splits: - name: train num_examples: 10408 - name: test num_examples: 2602 --- # Mozambique: Schools **Publisher:** OCHA Mozambique · **Source:** [HDX](https://data.humdata.org/dataset/mozambique-school-data) · **License:** `cc-by` · **Updated:** 2025-05-05 --- ## Abstract Mozambique school data at lowest administrative level available. Each row in this dataset represents geolocated point observations. Data was last updated on HDX on 2025-05-05. Geographic scope: **MOZ**. *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).* --- ## Dataset Characteristics | | | |---|---| | **Domain** | Education | | **Unit of observation** | Geolocated point observations | | **Rows (total)** | 13,010 | | **Columns** | 27 (6 numeric, 21 categorical, 0 datetime) | | **Train split** | 10,408 rows | | **Test split** | 2,602 rows | | **Geographic scope** | MOZ | | **Publisher** | OCHA Mozambique | | **HDX last updated** | 2025-05-05 | --- ## Variables **Geographic** — `latrina`, `totallatri` (range 0.0–58.0), `longitude` (range 30.2394–40.8219), `latitude` (range -26.8445–-10.5675). **Outcome / Measurement** — `totalurino` (range 0.0–30.0), `totalcb` (range 0.0–31.0). **Identifier / Metadata** — `localidade` (Cidade de Mocuba, Beira, Sede), `esa_source`, `esa_processed`. **Other** — `provincia` (Zambézia, Nampula, Tete), `distrito` (Milange, Gurué, Mocuba), `posto` (Milange sede, Nauela, Gurué Sede), `povoado` (Nenhum, Cidade de Nampula, Cidade de Mocuba), `cod_escola` (range 8104.0–620414.0) and 13 others. --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-mozambique-school-data") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `provincia` | object | 0.0% | Zambézia, Nampula, Tete | | `distrito` | object | 0.0% | Milange, Gurué, Mocuba | | `posto` | object | 0.0% | Milange sede, Nauela, Gurué Sede | | `localidade` | object | 0.0% | Cidade de Mocuba, Beira, Sede | | `povoado` | object | 0.1% | Nenhum, Cidade de Nampula, Cidade de Mocuba | | `cod_escola` | float64 | 13.9% | 8104.0 – 620414.0 (mean 67206.6694) | | `nomeesco_1` | object | 0.0% | Escola Primária Completa Eduardo Mondlane, Escola Primária Samora Machel, Escola Primária Completa 25 de Junho | | `zip` | object | 0.5% | Nenhum, Quême, Nihesiue | | `agua` | object | 0.0% | Não, Sim | | `tipofonte` | object | 0.0% | Sim, Nenhuma Fonte, Furo com bomba | | `qualiagua` | object | 0.0% | Nenhuma, Potável, Não Potável | | `energia` | object | 0.0% | | | `tipoenergi` | object | 0.0% | | | `latrina` | object | 0.0% | | | `totallatri` | float64 | 0.0% | 0.0 – 58.0 (mean 2.2881) | | `estado_de_conservação3` | object | 0.0% | | | `urinois` | object | 0.0% | | | `totalurino` | float64 | 0.0% | 0.0 – 30.0 (mean 1.1347) | | `urinoisfun` | object | 0.0% | | | `estado_de_conservação4` | object | 0.0% | | | `casabanho` | object | 0.0% | | | `totalcb` | float64 | 0.0% | 0.0 – 31.0 (mean 1.1361) | | `cbfunci` | object | 0.0% | | | `longitude` | float64 | 0.1% | 30.2394 – 40.8219 (mean 36.1759) | | `latitude` | float64 | 0.1% | -26.8445 – -10.5675 (mean -17.6031) | | `esa_source` | object | 0.0% | | | `esa_processed` | object | 0.0% | | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `cod_escola` | 8104.0 | 620414.0 | 67206.6694 | 70135.0 | | `totallatri` | 0.0 | 58.0 | 2.2881 | 2.0 | | `totalurino` | 0.0 | 30.0 | 1.1347 | 0.0 | | `totalcb` | 0.0 | 31.0 | 1.1361 | 0.0 | | `longitude` | 30.2394 | 40.8219 | 36.1759 | 35.9839 | | `latitude` | -26.8445 | -10.5675 | -17.6031 | -16.3178 | --- ## 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`. 12 exact duplicate rows were removed. 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 OCHA Mozambique 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/mozambique-school-data) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_mozambique_school_data, title = {Mozambique: Schools}, author = {OCHA Mozambique}, year = {2025}, url = {https://data.humdata.org/dataset/mozambique-school-data}, 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.*注释创建者：无注释 语言创建者：公开采集 语言：英语 许可证：CC BY 4.0 多语言属性：单语言 数据规模：10000 < 样本量 < 100000 源数据集：原创数据集 任务类别：其他 任务子类别：无 标签：非洲、人道主义、人道主义数据交换平台（HDX）、Electric Sheep Africa、教育、教育设施-学校、地理数据、莫桑比克（MOZ） 数据集名称：莫桑比克：学校数据集 数据集信息： 数据集划分： - 训练集：10408条样本 - 测试集：2602条样本 --- # 莫桑比克：学校数据集 **发布方：莫桑比克人道主义事务协调厅（OCHA Mozambique） · **数据源：[HDX](https://data.humdata.org/dataset/mozambique-school-data) · **许可证：`cc-by` · **更新时间：2025-05-05** --- ## 摘要 本数据集包含莫桑比克现有最低行政层级的学校点位数据。数据集中每一行均代表一个带地理定位的观测点位。本数据最后一次在HDX平台更新的时间为2025年5月5日。地理覆盖范围：**莫桑比克（MOZ）**。 *本数据集已由[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 | 类别 | 详情 | |---|---| | **研究领域** | 教育 | | **观测单元** | 带地理定位的点位观测值 | | **总数据行数** | 13010 | | **列数** | 27（6个数值型列、21个分类型列、0个日期时间型列） | | **训练集样本量** | 10408 | | **测试集样本量** | 2602 | | **地理覆盖范围** | 莫桑比克（MOZ） | | **发布方** | 莫桑比克人道主义事务协调厅 | | **HDX平台最后更新时间** | 2025-05-05 | --- ## 变量说明 ### 地理相关变量 `latrina`、`totallatri`（取值范围0.0–58.0）、`longitude`（经度，取值范围30.2394–40.8219）、`latitude`（纬度，取值范围-26.8445–-10.5675）。 ### 结果/测量变量 `totalurino`（取值范围0.0–30.0）、`totalcb`（取值范围0.0–31.0）。 ### 标识符与元数据变量 `localidade`（莫库巴市、贝拉、塞德）、`esa_source`、`esa_processed`。 ### 其他变量 `provincia`（赞比西亚、楠普拉、太特）、`distrito`（米兰热、古鲁埃、莫库巴）、`posto`（米兰热塞德、瑙埃拉、古鲁埃塞德）、`povoado`（恩亨乌姆、楠普拉市、莫库巴市）、`cod_escola`（取值范围8104.0–620414.0）及另外13个变量。 --- ## 快速入门 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-mozambique-school-data") 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 | 缺失率 | 取值范围/示例值 | |---|---|---|---| | `provincia` | 字符串型 | 0.0% | 赞比西亚、楠普拉、太特 | | `distrito` | 字符串型 | 0.0% | 米兰热、古鲁埃、莫库巴 | | `posto` | 字符串型 | 0.0% | 米兰热塞德、瑙埃拉、古鲁埃塞德 | | `localidade` | 字符串型 | 0.0% | 莫库巴市、贝拉、塞德 | | `povoado` | 字符串型 | 0.1% | 恩亨乌姆、楠普拉市、莫库巴市 | | `cod_escola` | 浮点型 | 13.9% | 8104.0 – 620414.0（均值67206.6694） | | `nomeesco_1` | 字符串型 | 0.0% | 爱德华多·蒙德拉纳完全小学、萨莫拉·马谢尔小学、6月25日完全小学 | | `zip` | 字符串型 | 0.5% | 恩亨乌姆、克梅、尼赫西乌 | | `agua` | 字符串型 | 0.0% | 否、是 | | `tipofonte` | 字符串型 | 0.0% | 是、无水源、带泵的水井 | | `qualiagua` | 字符串型 | 0.0% | 无、可饮用、不可饮用 | | `energia` | 字符串型 | 0.0% | | | `tipoenergi` | 字符串型 | 0.0% | | | `latrina` | 字符串型 | 0.0% | | | `totallatri` | 浮点型 | 0.0% | 0.0 – 58.0（均值2.2881） | | `estado_de_conservação3` | 字符串型 | 0.0% | | | `urinois` | 字符串型 | 0.0% | | | `totalurino` | 浮点型 | 0.0% | 0.0 – 30.0（均值1.1347） | | `urinoisfun` | 字符串型 | 0.0% | | | `estado_de_conservação4` | 字符串型 | 0.0% | | | `casabanho` | 字符串型 | 0.0% | | | `totalcb` | 浮点型 | 0.0% | 0.0 – 31.0（均值1.1361） | | `cbfunci` | 字符串型 | 0.0% | | | `longitude` | 浮点型 | 0.1% | 30.2394 – 40.8219（均值36.1759） | | `latitude` | 浮点型 | 0.1% | -26.8445 – -10.5675（均值-17.6031） | | `esa_source` | 字符串型 | 0.0% | | | `esa_processed` | 字符串型 | 0.0% | | --- ## 数值型变量汇总 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `cod_escola` | 8104.0 | 620414.0 | 67206.6694 | 70135.0 | | `totallatri` | 0.0 | 58.0 | 2.2881 | 2.0 | | `totalurino` | 0.0 | 30.0 | 1.1347 | 0.0 | | `totalcb` | 0.0 | 31.0 | 1.1361 | 0.0 | | `longitude` | 30.2394 | 40.8219 | 36.1759 | 35.9839 | | `latitude` | -26.8445 | -10.5675 | -17.6031 | -16.3178 | --- ## 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`。删除了12条完全重复的样本。基于解析成功率（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，并保存为Snappy压缩的Parquet格式。 --- ## 局限性 - 本数据源自莫桑比克人道主义事务协调厅，未经Electric Sheep Africa独立验证。 - 自动化清洗流程无法修正原始数据收集阶段的错报值、定义不一致或抽样偏差问题。 - 请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/mozambique-school-data)获取发布方提供的方法论说明与免责条款。 --- ## 引用格式 bibtex @dataset{hdx_africa_mozambique_school_data, title = {莫桑比克：学校数据集}, author = {莫桑比克人道主义事务协调厅}, year = {2025}, url = {https://data.humdata.org/dataset/mozambique-school-data}, note = {由Electric Sheep Africa重新打包以适配机器学习使用（https://huggingface.co/electricsheepafrica）} } --- *[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。*