electricsheepafrica/africa-zimbabwe-schools-in-zimbabwe

--- 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: - other task_ids: [] tags: - africa - humanitarian - hdx - electric-sheep-africa - education - education-facilities-schools - facilities-infrastructure - hxl - zwe pretty_name: "Zimbabwe: Schools" dataset_info: splits: - name: train num_examples: 7823 - name: test num_examples: 1955 --- # Zimbabwe: Schools **Publisher:** OCHA Regional Office for Southern and Eastern Africa (ROSEA) · **Source:** [HDX](https://data.humdata.org/dataset/zimbabwe-schools-in-zimbabwe) · **License:** `cc-by` · **Updated:** 2025-04-10 --- ## Abstract Schools and learning facilities in Zimbabwe Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2025-04-10. Geographic scope: **ZWE**. *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).* --- ## Dataset Characteristics | | | |---|---| | **Domain** | Education | | **Unit of observation** | Subnational administrative unit observations | | **Rows (total)** | 9,779 | | **Columns** | 10 (3 numeric, 7 categorical, 0 datetime) | | **Train split** | 7,823 rows | | **Test split** | 1,955 rows | | **Geographic scope** | ZWE | | **Publisher** | OCHA Regional Office for Southern and Eastern Africa (ROSEA) | | **HDX last updated** | 2025-04-10 | --- ## Variables **Geographic** — `province` (Manicaland, Midlands, Masvingo), `district` (Mutare, Makoni, Hurungwe), `latitude` (range -22.3313–0.0), `longitude` (range 0.0–33.0239). **Outcome / Measurement** — `schoolnumber` (range 1001.0–45632.0). **Identifier / Metadata** — `name` (KUSHINGA, RUSUNUNGUKO, BATANAI), `esa_source` (HDX), `esa_processed` (2026-04-18). **Other** — `schoollevel` (Primary, Secondary, #loc+school+type), `grant_class` (P3, S3, P2). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-zimbabwe-schools-in-zimbabwe") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `schoolnumber` | float64 | 0.0% | 1001.0 – 45632.0 (mean 9070.2503) | | `name` | object | 0.0% | KUSHINGA, RUSUNUNGUKO, BATANAI | | `province` | object | 0.0% | Manicaland, Midlands, Masvingo | | `schoollevel` | object | 0.0% | Primary, Secondary, #loc+school+type | | `district` | object | 0.0% | Mutare, Makoni, Hurungwe | | `grant_class` | object | 0.0% | P3, S3, P2 | | `latitude` | float64 | 19.0% | -22.3313 – 0.0 (mean -18.8668) | | `longitude` | float64 | 10.8% | 0.0 – 33.0239 (mean 27.6846) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-18 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `schoolnumber` | 1001.0 | 45632.0 | 9070.2503 | 8913.5 | | `latitude` | -22.3313 | 0.0 | -18.8668 | -18.7803 | | `longitude` | 0.0 | 33.0239 | 27.6846 | 30.5293 | --- ## 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 OCHA Regional Office for Southern and Eastern Africa (ROSEA) 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/zimbabwe-schools-in-zimbabwe) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_zimbabwe_schools_in_zimbabwe, title = {Zimbabwe: Schools}, author = {OCHA Regional Office for Southern and Eastern Africa (ROSEA)}, year = {2025}, url = {https://data.humdata.org/dataset/zimbabwe-schools-in-zimbabwe}, 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（no-annotation） language_creators: - 现有文本采集（found） language: - 英语（en） license: CC-BY-4.0 multilinguality: - 单语言（monolingual） size_categories: - 1000<n<10000 source_datasets: - 原创数据集（original） task_categories: - 其他（other） task_ids: - 无 tags: - 非洲（africa） - 人道主义（humanitarian） - HDX（Humanitarian Data Exchange） - electric-sheep-africa - 教育（education） - 教育设施-学校（education-facilities-schools） - 设施-基础设施（facilities-infrastructure） - hxl - 津巴布韦（zwe） pretty_name: "津巴布韦：学校" dataset_info: splits: - name: 训练集（train） num_examples: 7823 - name: 测试集（test） num_examples: 1955 # 津巴布韦：学校数据集 **发布方：** 联合国人道主义事务协调厅（Office for the Coordination of Humanitarian Affairs, OCHA）南部与东部非洲区域办事处（ROSEA） · **来源：** [HDX（Humanitarian Data Exchange）](https://data.humdata.org/dataset/zimbabwe-schools-in-zimbabwe) · **许可协议：** `CC-BY` · **最后更新：** 2025-04-10 --- ## 摘要 本数据集收录津巴布韦境内的学校与学习设施信息。数据集中每一行代表一个次国家级行政单元的观测记录。本数据集最近一次在HDX平台更新的时间为2025-04-10。地理覆盖范围：**津巴布韦（ZWE）**。 *本数据集由[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 | | | |---|---| | **领域** | 教育 | | **观测单元** | 次国家级行政单元观测记录 | | **总数据行数** | 9779 | | **列数** | 10列（3个数值型、7个分类型、0个日期时间型） | | **训练集划分** | 7823行 | | **测试集划分** | 1955行 | | **地理覆盖范围** | 津巴布韦（ZWE） | | **发布方** | 联合国人道主义事务协调厅南部与东部非洲区域办事处（ROSEA） | | **HDX平台最后更新时间** | 2025-04-10 | --- ## 变量说明 **地理类变量** — `province`（省份：马尼卡兰、中部省、马斯温戈），`district`（区县：穆塔雷、马科尼、胡伦圭），`latitude`（纬度：取值范围-22.3313~0.0），`longitude`（经度：取值范围0.0~33.0239）。 **结果/测量类变量** — `schoolnumber`（学校编号：取值范围1001.0~45632.0）。 **标识符/元数据类变量** — `name`（学校名称：库欣加、鲁苏农古科、巴塔奈），`esa_source`（数据来源：HDX），`esa_processed`（数据处理时间：2026-04-18）。 **其他变量** — `schoollevel`（学校层级：小学、中学、#loc+school+type），`grant_class`（资助等级：P3、S3、P2）。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-zimbabwe-schools-in-zimbabwe") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() --- ## 数据结构（Schema） | 列名 | 数据类型 | 缺失率 | 取值范围/示例值 | |---|---|---|---| | `schoolnumber` | float64 | 0.0% | 1001.0 – 45632.0（均值 9070.2503） | | `name` | object | 0.0% | KUSHINGA, RUSUNUNGUKO, BATANAI | | `province` | object | 0.0% | 马尼卡兰、中部省、马斯温戈 | | `schoollevel` | object | 0.0% | 小学、中学、#loc+school+type | | `district` | object | 0.0% | 穆塔雷、马科尼、胡伦圭 | | `grant_class` | object | 0.0% | P3、S3、P2 | | `latitude` | float64 | 19.0% | -22.3313 – 0.0（均值 -18.8668） | | `longitude` | float64 | 10.8% | 0.0 – 33.0239（均值 27.6846） | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-18 | --- ## 数值型变量统计摘要 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `schoolnumber` | 1001.0 | 45632.0 | 9070.2503 | 8913.5 | | `latitude` | -22.3313 | 0.0 | -18.8668 | -18.7803 | | `longitude` | 0.0 | 33.0239 | 27.6846 | 30.5293 | --- ## 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规范为蛇形命名法（snake_case）。常见缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）被统一替换为`NaN`。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，并保存为Snappy压缩格式的Parquet文件。 --- ## 数据集局限性 - 本数据集源自联合国人道主义事务协调厅南部与东部非洲区域办事处（ROSEA），尚未由Electric Sheep Africa进行独立验证。 - 自动化清洗流程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。 - 请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/zimbabwe-schools-in-zimbabwe)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_zimbabwe_schools_in_zimbabwe, title = {津巴布韦：学校}, author = {联合国人道主义事务协调厅南部与东部非洲区域办事处（ROSEA）}, year = {2025}, url = {https://data.humdata.org/dataset/zimbabwe-schools-in-zimbabwe}, note = {由Electric Sheep Africa（https://huggingface.co/electricsheepafrica）重新打包以适配机器学习任务} } --- *[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — 非洲机器学习数据集基础设施提供商，总部位于尼日利亚拉各斯。*