electricsheepafrica/africa-demographics-eritrea
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--- annotations_creators: - no-annotation language_creators: - found language: - en license: other multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - tabular-classification - other task_ids: [] tags: - africa - humanitarian - hdx - electric-sheep-africa - demographics - health - eri pretty_name: "Eritrea - Subnational Demographic and Health Data" dataset_info: splits: - name: train num_examples: 201 - name: test num_examples: 50 --- # Eritrea - Subnational Demographic and Health Data **Publisher:** The DHS Program · **Source:** [HDX](https://data.humdata.org/dataset/dhs-subnational-data-for-eritrea) · **License:** `hdx-other` · **Updated:** 2026-04-20 --- ## Abstract Contains data from the [DHS data portal](https://api.dhsprogram.com/). There is also a dataset containing [Eritrea - National Demographic and Health Data](https://data.humdata.org/dataset/dhs-data-for-eritrea) on HDX. The DHS Program Application Programming Interface (API) provides software developers access to aggregated indicator data from The Demographic and Health Surveys (DHS) Program. The API can be used to create various applications to help analyze, visualize, explore and disseminate data on population, health, HIV, and nutrition from more than 90 countries. Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-04-20. Geographic scope: **ERI**. *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).* --- ## Dataset Characteristics | | | |---|---| | **Domain** | Public health | | **Unit of observation** | First-level administrative unit observations | | **Rows (total)** | 252 | | **Columns** | 30 (14 numeric, 16 categorical, 0 datetime) | | **Train split** | 201 rows | | **Test split** | 50 rows | | **Geographic scope** | ERI | | **Publisher** | The DHS Program | | **HDX last updated** | 2026-04-20 | --- ## Variables **Geographic** — `iso3` (ERI), `location` (Southern Red Sea, Northern Red Sea, Anseba), `dhs_countrycode` (ER), `countryname` (Eritrea), `surveyyear` (range 1995.0–2002.0) and 8 others. **Outcome / Measurement** — `value` (range 0.5–239.0), `istotal` (range 0.0–0.0). **Identifier / Metadata** — `dataid` (range 39517.0–7962888.0), `indicatorid` (RH_DELP_C_DHF, CH_DIAT_C_ORT, CH_VACC_C_BAS), `characteristicid` (range 404001.0–404006.0), `characteristiclabel` (Southern Red Sea, Northern Red Sea, Anseba), `ispreferred` (range 0.0–1.0) and 3 others. **Other** — `indicator` (Place of delivery: Health facility, Treatment of diarrhea: Either ORS or RHF, Fully vaccinated (8 basic antigens)), `precision` (range 0.0–1.0), `indicatororder` (range 11763080.0–260321010.0), `characteristicorder` (range 1404001.0–1404006.0), `denominatorweighted` (range 11.0–2763.0) and 2 others. --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-demographics-eritrea") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `iso3` | object | 0.0% | ERI | | `location` | object | 0.0% | Southern Red Sea, Northern Red Sea, Anseba | | `dataid` | int64 | 0.0% | 39517.0 – 7962888.0 (mean 4154143.3056) | | `indicator` | object | 0.0% | Place of delivery: Health facility, Treatment of diarrhea: Either ORS or RHF, Fully vaccinated (8 basic antigens) | | `value` | float64 | 0.0% | 0.5 – 239.0 (mean 35.5591) | | `precision` | int64 | 0.0% | 0.0 – 1.0 (mean 0.9048) | | `dhs_countrycode` | object | 0.0% | ER | | `countryname` | object | 0.0% | Eritrea | | `surveyyear` | int64 | 0.0% | 1995.0 – 2002.0 (mean 1998.6667) | | `surveyid` | object | 0.0% | ER2002DHS, ER1995DHS | | `indicatorid` | object | 0.0% | RH_DELP_C_DHF, CH_DIAT_C_ORT, CH_VACC_C_BAS | | `indicatororder` | int64 | 0.0% | 11763080.0 – 260321010.0 (mean 91556205.4762) | | `indicatortype` | object | 0.0% | I | | `characteristicid` | int64 | 0.0% | 404001.0 – 404006.0 (mean 404003.5) | | `characteristicorder` | int64 | 0.0% | 1404001.0 – 1404006.0 (mean 1404003.5) | | `characteristiccategory` | object | 0.0% | Region | | `characteristiclabel` | object | 0.0% | Southern Red Sea, Northern Red Sea, Anseba | | `byvariableid` | int64 | 0.0% | 0.0 – 14003.0 (mean 4000.4286) | | `byvariablelabel` | object | 71.4% | | | `istotal` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) | | `ispreferred` | int64 | 0.0% | 0.0 – 1.0 (mean 0.881) | | `sdrid` | object | 0.0% | | | `regionid` | object | 0.0% | | | `surveyyearlabel` | int64 | 0.0% | 1995.0 – 2002.0 (mean 1998.6667) | | `surveytype` | object | 0.0% | | | `denominatorweighted` | float64 | 28.6% | 11.0 – 2763.0 (mean 645.4889) | | `denominatorunweighted` | float64 | 28.6% | 27.0 – 1888.0 (mean 640.7611) | | `levelrank` | int64 | 0.0% | 1.0 – 1.0 (mean 1.0) | | `esa_source` | object | 0.0% | | | `esa_processed` | object | 0.0% | | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `dataid` | 39517.0 | 7962888.0 | 4154143.3056 | 4105188.0 | | `value` | 0.5 | 239.0 | 35.5591 | 20.2 | | `precision` | 0.0 | 1.0 | 0.9048 | 1.0 | | `surveyyear` | 1995.0 | 2002.0 | 1998.6667 | 2002.0 | | `indicatororder` | 11763080.0 | 260321010.0 | 91556205.4762 | 83566070.0 | | `characteristicid` | 404001.0 | 404006.0 | 404003.5 | 404003.5 | | `characteristicorder` | 1404001.0 | 1404006.0 | 1404003.5 | 1404003.5 | | `byvariableid` | 0.0 | 14003.0 | 4000.4286 | 0.0 | | `istotal` | 0.0 | 0.0 | 0.0 | 0.0 | | `ispreferred` | 0.0 | 1.0 | 0.881 | 1.0 | | `surveyyearlabel` | 1995.0 | 2002.0 | 1998.6667 | 2002.0 | | `denominatorweighted` | 11.0 | 2763.0 | 645.4889 | 453.0 | | `denominatorunweighted` | 27.0 | 1888.0 | 640.7611 | 562.5 | | `levelrank` | 1.0 | 1.0 | 1.0 | 1.0 | --- ## 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`. 2 column(s) with >80% missing values were removed: `cilow`, `cihigh`. 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 The DHS Program and has not been independently validated by ESA. - Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection. - The following columns have >20% missing values and should be treated with caution in modelling: `byvariablelabel`, `denominatorweighted`, `denominatorunweighted`. - Refer to the [original HDX dataset page](https://data.humdata.org/dataset/dhs-subnational-data-for-eritrea) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_demographics_eritrea, title = {Eritrea - Subnational Demographic and Health Data}, author = {The DHS Program}, year = {2026}, url = {https://data.humdata.org/dataset/dhs-subnational-data-for-eritrea}, 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：现有资源抓取 语言：英语 许可协议：其他 多语言属性：单语言 数据规模：少于1000条 源数据集：原始数据集 任务类别：表格分类、其他 任务子类别：无 标签：非洲、人道主义、HDX（人道主义数据交换平台）、Electric Sheep Africa（电羊非洲）、人口统计学、健康、ERI（厄立特里亚） 展示名称："厄立特里亚——次国家人口与健康数据" 数据集信息： 划分： - 名称：训练集 样本数：201 - 名称：测试集 样本数：50 --- # 厄立特里亚——次国家人口与健康数据集 **发布方**：人口与健康调查项目（Demographic and Health Surveys Program，DHS Program） · **来源**：[HDX（人道主义数据交换平台）](https://data.humdata.org/dataset/dhs-subnational-data-for-eritrea) · **许可协议**：`hdx-other` · **最后更新时间**：2026-04-20 --- ## 摘要 本数据集数据源自[DHS数据门户](https://api.dhsprogram.com/)。HDX平台上另有一份包含[厄立特里亚国家人口与健康数据](https://data.humdata.org/dataset/dhs-data-for-eritrea)的数据集。 人口与健康调查项目（DHS Program）应用程序编程接口（API）可为软件开发人员提供来自该项目的聚合指标数据。借助该API可开发各类应用程序，用于分析、可视化、探索并传播全球90余个国家的人口、健康、艾滋病病毒（HIV）及营养相关数据。 本数据集的每一行数据均代表一级行政单元的观测值。数据集最后于2026-04-20在HDX平台更新。地理覆盖范围：**ERI（厄立特里亚）**。 *本数据集已由[电羊非洲（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** | 公共卫生 | | **观测单元** | 一级行政单元观测值 | | **总数据行数** | 252 | | **列数** | 30列（14列数值型、16列分类型、0列日期型） | | **训练集划分** | 201行 | | **测试集划分** | 50行 | | **地理覆盖范围** | ERI（厄立特里亚） | | **发布方** | 人口与健康调查项目（DHS Program） | | **HDX平台最后更新时间** | 2026-04-20 | --- ## 变量 **地理类变量** — `iso3`（国家代码ERI，厄立特里亚）、`location`（地区：南红海省、北红海省、安塞巴省）、`dhs_countrycode`（DHS国家代码ER）、`countryname`（国家名称：厄立特里亚）、`surveyyear`（调查年份范围：1995.0–2002.0），另有8个其他地理变量。 **结果/测量类变量** — `value`（数值范围：0.5–239.0）、`istotal`（数值范围：0.0–0.0）。 **标识符/元数据类变量** — `dataid`（数值范围：39517.0–7962888.0）、`indicatorid`（指标代码：RH_DELP_C_DHF、CH_DIAT_C_ORT、CH_VACC_C_BAS）、`characteristicid`（特征ID范围：404001.0–404006.0）、`characteristiclabel`（特征标签：南红海省、北红海省、安塞巴省）、`ispreferred`（数值范围：0.0–1.0），另有3个其他标识符变量。 **其他变量** — `indicator`（指标：分娩地点：医疗机构、腹泻治疗：口服补液盐（ORS）或补液疗法（RHF）、完全接种8种基础抗原）、`precision`（精度范围：0.0–1.0）、`indicatororder`（指标排序范围：11763080.0–260321010.0）、`characteristicorder`（特征排序范围：1404001.0–1404006.0）、`denominatorweighted`（加权分母范围：11.0–2763.0），另有2个其他变量。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-demographics-eritrea") 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 | |---|---|---|---| | `iso3` | 字符串（object） | 0.0% | ERI | | `location` | 字符串（object） | 0.0% | 南红海省、北红海省、安塞巴省 | | `dataid` | 64位整数（int64） | 0.0% | 39517.0 – 7962888.0（均值：4154143.3056） | | `indicator` | 字符串（object） | 0.0% | 分娩地点：医疗机构、腹泻治疗：口服补液盐（ORS）或补液疗法（RHF）、完全接种8种基础抗原 | | `value` | 64位浮点数（float64） | 0.0% | 0.5 – 239.0（均值：35.5591） | | `precision` | 64位整数（int64） | 0.0% | 0.0 – 1.0（均值：0.9048） | | `dhs_countrycode` | 字符串（object） | 0.0% | ER | | `countryname` | 字符串（object） | 0.0% | 厄立特里亚 | | `surveyyear` | 64位整数（int64） | 0.0% | 1995.0 – 2002.0（均值：1998.6667） | | `surveyid` | 字符串（object） | 0.0% | ER2002DHS、ER1995DHS | | `indicatorid` | 字符串（object） | 0.0% | RH_DELP_C_DHF、CH_DIAT_C_ORT、CH_VACC_C_BAS | | `indicatororder` | 64位整数（int64） | 0.0% | 11763080.0 – 260321010.0（均值：91556205.4762） | | `indicatortype` | 字符串（object） | 0.0% | I | | `characteristicid` | 64位整数（int64） | 0.0% | 404001.0 – 404006.0（均值：404003.5） | | `characteristicorder` | 64位整数（int64） | 0.0% | 1404001.0 – 1404006.0（均值：1404003.5） | | `characteristiccategory` | 字符串（object） | 0.0% | 地区 | | `characteristiclabel` | 字符串（object） | 0.0% | 南红海省、北红海省、安塞巴省 | | `byvariableid` | 64位整数（int64） | 0.0% | 0.0 – 14003.0（均值：4000.4286） | | `byvariablelabel` | 字符串（object） | 71.4% | 无 | | `istotal` | 64位整数（int64） | 0.0% | 0.0 – 0.0（均值：0.0） | | `ispreferred` | 64位整数（int64） | 0.0% | 0.0 – 1.0（均值：0.881） | | `sdrid` | 字符串（object） | 0.0% | 无 | | `regionid` | 字符串（object） | 0.0% | 无 | | `surveyyearlabel` | 64位整数（int64） | 0.0% | 1995.0 – 2002.0（均值：1998.6667） | | `surveytype` | 字符串（object） | 0.0% | 无 | | `denominatorweighted` | 64位浮点数（float64） | 28.6% | 11.0 – 2763.0（均值：645.4889） | | `denominatorunweighted` | 64位浮点数（float64） | 28.6% | 27.0 – 1888.0（均值：640.7611） | | `levelrank` | 64位整数（int64） | 0.0% | 1.0 – 1.0（均值：1.0） | | `esa_source` | 字符串（object） | 0.0% | 无 | | `esa_processed` | 字符串（object） | 0.0% | 无 | --- ## 数值统计摘要 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `dataid` | 39517.0 | 7962888.0 | 4154143.3056 | 4105188.0 | | `value` | 0.5 | 239.0 | 35.5591 | 20.2 | | `precision` | 0.0 | 1.0 | 0.9048 | 1.0 | | `surveyyear` | 1995.0 | 2002.0 | 1998.6667 | 2002.0 | | `indicatororder` | 11763080.0 | 260321010.0 | 91556205.4762 | 83566070.0 | | `characteristicid` | 404001.0 | 404006.0 | 404003.5 | 404003.5 | | `characteristicorder` | 1404001.0 | 1404006.0 | 1404003.5 | 1404003.5 | | `byvariableid` | 0.0 | 14003.0 | 4000.4286 | 0.0 | | `istotal` | 0.0 | 0.0 | 0.0 | 0.0 | | `ispreferred` | 0.0 | 1.0 | 0.881 | 1.0 | | `surveyyearlabel` | 1995.0 | 2002.0 | 1998.6667 | 2002.0 | | `denominatorweighted` | 11.0 | 2763.0 | 645.4889 | 453.0 | | `denominatorunweighted` | 27.0 | 1888.0 | 640.7611 | 562.5 | | `levelrank` | 1.0 | 1.0 | 1.0 | 1.0 | --- ## 数据整理流程 原始数据通过CKAN应用程序编程接口（API）从HDX平台下载，并转换为Parquet格式。所有列名均转为小写并统一为蛇形命名法（snake_case）。常见缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）均被统一替换为`NaN`。删除了2个缺失值占比超过80%的列：`cilow`与`cihigh`。本数据集以固定随机种子（42）按80/20比例划分为训练集与测试集，并以Snappy压缩的Parquet格式存储。 --- ## 局限性 - 本数据集数据源自人口与健康调查项目（DHS Program），未经电羊非洲（ESA）独立验证。 - 自动化数据清洗无法修正原始数据收集中的报告错误、定义不一致或抽样偏差问题。 - 以下列的缺失值占比超过20%，在建模过程中需谨慎使用：`byvariablelabel`、`denominatorweighted`、`denominatorunweighted`。 - 如需了解发布方的方法说明与注意事项，请参阅[HDX平台原始数据集页面](https://data.humdata.org/dataset/dhs-subnational-data-for-eritrea)。 --- ## 引用 bibtex @dataset{hdx_africa_demographics_eritrea, title = {Eritrea - Subnational Demographic and Health Data}, author = {The DHS Program}, year = {2026}, url = {https://data.humdata.org/dataset/dhs-subnational-data-for-eritrea}, 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。*
