electricsheepafrica/africa-ethiopia-pin-targeted-reached-by-location-and-cluster
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--- annotations_creators: - no-annotation language_creators: - found language: - en license: cc-by-4.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - tabular-classification - other task_ids: [] tags: - africa - humanitarian - hdx - electric-sheep-africa - affected-population - drought - hxl - people-in-need-pin - eth pretty_name: "Ethiopia Drought Related - People Affected, Targeted & Reached by Location" dataset_info: splits: - name: train num_examples: 312 - name: test num_examples: 78 --- # Ethiopia Drought Related - People Affected, Targeted & Reached by Location **Publisher:** OCHA Regional Office for Southern and Eastern Africa (ROSEA) · **Source:** [HDX](https://data.humdata.org/dataset/ethiopia-pin-targeted-reached-by-location-and-cluster) · **License:** `cc-by` · **Updated:** 2025-09-16 --- ## Abstract Drought affected areas and population in Ethiopia Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-09-16. Geographic scope: **ETH**. *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).* --- ## Dataset Characteristics | | | |---|---| | **Domain** | Natural hazards and disaster risk | | **Unit of observation** | Tabular records | | **Rows (total)** | 391 | | **Columns** | 12 (4 numeric, 8 categorical, 0 datetime) | | **Train split** | 312 rows | | **Test split** | 78 rows | | **Geographic scope** | ETH | | **Publisher** | OCHA Regional Office for Southern and Eastern Africa (ROSEA) | | **HDX last updated** | 2025-09-16 | --- ## Variables **Geographic** — `location` (admin3Pcode, ET050586, ET050788), `operational_priority` (range 1.0–3.0). **Identifier / Metadata** — `unnamed_1` (Woreda, Marsin, Daratole), `unnamed_2` (East Hararge, West Hararge, Guji), `unnamed_3` (ET0410, ET0409, ET0414), `unnamed_4` (Oromia, Somali, SNNP), `unnamed_5` (ET04, ET05, ET07) and 4 others. **Other** — `overall_figures` (range 23.0–283966.0). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-ethiopia-pin-targeted-reached-by-location-and-cluster") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `location` | object | 0.0% | admin3Pcode, ET050586, ET050788 | | `unnamed_1` | object | 0.0% | Woreda, Marsin, Daratole | | `unnamed_2` | object | 0.0% | East Hararge, West Hararge, Guji | | `unnamed_3` | object | 0.0% | ET0410, ET0409, ET0414 | | `unnamed_4` | object | 0.0% | Oromia, Somali, SNNP | | `unnamed_5` | object | 0.0% | ET04, ET05, ET07 | | `operational_priority` | float64 | 0.5% | 1.0 – 3.0 (mean 2.2391) | | `overall_figures` | float64 | 0.5% | 23.0 – 283966.0 (mean 41606.928) | | `unnamed_8` | float64 | 0.5% | 104.0 – 342553.0 (mean 33482.9846) | | `unnamed_9` | float64 | 0.5% | 0.0 – 255985.0 (mean 20282.5501) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-10 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `operational_priority` | 1.0 | 3.0 | 2.2391 | 2.0 | | `overall_figures` | 23.0 | 283966.0 | 41606.928 | 30987.0 | | `unnamed_8` | 104.0 | 342553.0 | 33482.9846 | 21742.0 | | `unnamed_9` | 0.0 | 255985.0 | 20282.5501 | 5508.0 | --- ## 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`. 4 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/ethiopia-pin-targeted-reached-by-location-and-cluster) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_ethiopia_pin_targeted_reached_by_location_and_cluster, title = {Ethiopia Drought Related - People Affected, Targeted & Reached by Location}, author = {OCHA Regional Office for Southern and Eastern Africa (ROSEA)}, year = {2025}, url = {https://data.humdata.org/dataset/ethiopia-pin-targeted-reached-by-location-and-cluster}, 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: - 现有采集 language: - 英语 license: CC BY 4.0 multilinguality: - 单语言 size_categories: - 样本量<1000 source_datasets: - 原创数据集 task_categories: - 表格分类 - 其他 task_ids: [] tags: - 非洲 - 人道主义 - HDX（Humanitarian Data Exchange） - Electric Sheep Africa - 受影响人口 - 干旱 - HXL（Humanitarian Exchange Language） - 需要援助人口（People In Need，简称PIN） - 埃塞俄比亚（ETH） pretty_name: "埃塞俄比亚干旱相关——按地区划分的受影响、目标援助与已覆盖人口" dataset_info: splits: - name: train num_examples: 312 - name: test num_examples: 78 # 埃塞俄比亚干旱相关——按地区划分的受影响、目标援助与已覆盖人口 **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） · **来源**：[HDX（Humanitarian Data Exchange）](https://data.humdata.org/dataset/ethiopia-pin-targeted-reached-by-location-and-cluster) · **许可协议**：`CC BY` · **更新时间**：2025-09-16 --- ## 摘要 埃塞俄比亚受干旱影响的地区与人口 本数据集的每一行均为表格记录。数据最近一次在HDX平台更新的时间为2025-09-16。地理覆盖范围：**埃塞俄比亚（ETH）**。 *本数据集已由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为可供机器学习使用的Parquet格式。* --- ## 数据集特征 | | | |---|---| | **领域** | 自然灾害与灾害风险防控 | | **观测单元** | 表格记录 | | **总记录数** | 391条 | | **字段数** | 12个（4个数值型，8个分类型，0个日期时间型） | | **训练集划分** | 312条记录 | | **测试集划分** | 78条记录 | | **地理覆盖范围** | 埃塞俄比亚（ETH） | | **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** | 2025-09-16 | --- ## 变量说明 **地理类**：`location`（行政3级编码，ET050586、ET050788）、`operational_priority`（取值范围1.0–3.0）。 **标识符与元数据类**：`unnamed_1`（沃莱达（Woreda，埃塞俄比亚基层行政单位），取值包括Marsin、Daratole）、`unnamed_2`（东哈勒盖、西哈勒盖、古吉）、`unnamed_3`（ET0410、ET0409、ET0414）、`unnamed_4`（奥罗米亚、索马里、SNNP）、`unnamed_5`（ET04、ET05、ET07）及另外4个字段。 **其他类**：`overall_figures`（取值范围23.0–283966.0）。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-ethiopia-pin-targeted-reached-by-location-and-cluster") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() --- ## 字段结构 | 字段名 | 数据类型 | 空值占比 | 取值范围/示例值 | |---|---|---|---| | `location` | 字符串（object） | 0.0% | 行政3级编码、ET050586、ET050788 | | `unnamed_1` | 字符串（object） | 0.0% | 沃莱达（Woreda）、Marsin、Daratole | | `unnamed_2` | 字符串（object） | 0.0% | 东哈勒盖、西哈勒盖、古吉 | | `unnamed_3` | 字符串（object） | 0.0% | ET0410、ET0409、ET0414 | | `unnamed_4` | 字符串（object） | 0.0% | 奥罗米亚、索马里、SNNP | | `unnamed_5` | 字符串（object） | 0.0% | ET04、ET05、ET07 | | `operational_priority` | 浮点型（float64） | 0.5% | 1.0 – 3.0（均值2.2391） | | `overall_figures` | 浮点型（float64） | 0.5% | 23.0 – 283966.0（均值41606.928） | | `unnamed_8` | 浮点型（float64） | 0.5% | 104.0 – 342553.0（均值33482.9846） | | `unnamed_9` | 浮点型（float64） | 0.5% | 0.0 – 255985.0（均值20282.5501） | | `esa_source` | 字符串（object） | 0.0% | HDX | | `esa_processed` | 字符串（object） | 0.0% | 2026-04-10 | --- ## 数值型字段统计摘要 | 字段名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `operational_priority` | 1.0 | 3.0 | 2.2391 | 2.0 | | `overall_figures` | 23.0 | 283966.0 | 41606.928 | 30987.0 | | `unnamed_8` | 104.0 | 342553.0 | 33482.9846 | 21742.0 | | `unnamed_9` | 0.0 | 255985.0 | 20282.5501 | 5508.0 | --- ## 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。所有字段名均转换为小写并标准化为蛇形命名法。通用缺失值标记（`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4个字段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。 --- ## 局限性说明 - 本数据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），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、定义不一致或采样偏差问题。 - 如需查看发布方提供的方法论说明与免责条款，请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/ethiopia-pin-targeted-reached-by-location-and-cluster)。 --- ## 引用格式 bibtex @dataset{hdx_africa_ethiopia_pin_targeted_reached_by_location_and_cluster, title = {埃塞俄比亚干旱相关——按地区划分的受影响、目标援助与已覆盖人口}, 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/ethiopia-pin-targeted-reached-by-location-and-cluster}, note = {由Electric Sheep Africa（https://huggingface.co/electricsheepafrica）重新打包以适配机器学习使用} } --- *[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。*
