electricsheepafrica/africa-gnq-rainfall-subnational
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--- 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: - tabular-regression - other task_ids: [] tags: - africa - humanitarian - hdx - electric-sheep-africa - climate-weather - environment - gnq pretty_name: "Equatorial Guinea: Rainfall Indicators at Subnational Level" dataset_info: splits: - name: train num_examples: 18256 - name: test num_examples: 4564 --- # Equatorial Guinea: Rainfall Indicators at Subnational Level **Publisher:** WFP - World Food Programme · **Source:** [HDX](https://data.humdata.org/dataset/gnq-rainfall-subnational) · **License:** `cc-by` · **Updated:** 2026-04-03 --- ## Abstract This dataset contains dekadal rainfall indicators, computed from Climate Hazards Group InfraRed Precipitation satellite imagery with insitu Station data (CHIRPS) version 2 and the CHIRPS-GEFS short term rainfall forecasts, aggregated by subnational administrative units. Included indicators are (for each dekad): - 10 day rainfall [mm] (`rfh`) - rainfall 1-month rolling aggregation [mm] (`r1h`) - rainfall 3-month rolling aggregation [mm] (`r3h`) - rainfall long term average [mm] (`rfh_avg`) - rainfall 1-month rolling aggregation long term average [mm] (`r1h_avg`) - rainfall 3-month rolling aggregation long term average [mm] (`r3h_avg`) - rainfall anomaly [%] (`rfq`) - rainfall 1-month anomaly [%] (`r1q`) - rainfall 3-month anomaly [%] (`r3q`) The administrative units used for aggregation are based on WFP data and contain a Pcode reference attributed to each unit. The number of input pixels used to create the aggregates, is provided in the `n_pixels` column. Finally, the `type` column indicates if the value is based on a forecast, a preliminary or a final product. Forecasts are issued on the 6th, 16th, and 26th of each month for the upcoming 10-day period (dekad), then updated with improved versions on the 1st, 11th, and 21st. Preliminary observations replace the previous dekad’s forecast on the 3rd, 13th, and 23rd, and are later replaced by final observations—published mid-month (13th or 23rd)—covering all three dekads of the prior month. Please find a summary below: Publication Day: Forecast type, Covers (Dekad) - 1st: Updated forecast, 1–10 of the same month - 6th: Initial forecast, 11–20 of the same month - 11th: Updated forecast, 1–10 of the same month - 16th: Initial forecast, 21–end of the same month - 21st: Updated forecast, 11–20 of the same month - 26th: Initial forecast, 1–10 of the following month For more on CHIRPS-GEFS forecasts, see: https://www.chc.ucsb.edu/data/chirps-gefs For further details, please see the methodology section. Each row in this dataset represents time-series observations. Temporal coverage is indicated by the `date` column(s). Geographic scope: **GNQ**. *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).* --- ## Dataset Characteristics | | | |---|---| | **Domain** | Climate and environment | | **Unit of observation** | Time-series observations | | **Rows (total)** | 22,820 | | **Columns** | 17 (12 numeric, 4 categorical, 1 datetime) | | **Train split** | 18,256 rows | | **Test split** | 4,564 rows | | **Geographic scope** | GNQ | | **Publisher** | WFP - World Food Programme | | **HDX last updated** | 2026-04-03 | --- ## Variables **Geographic** — `n_pixels` (range 1.0–273.0). **Temporal** — `date`. **Identifier / Metadata** — `adm_id` (range 1198.0–15834.0), `pcode` (GQ198, GQ199, GQ200), `esa_source` (HDX), `esa_processed` (2026-04-07). **Other** — `adm_level` (range 1.0–2.0), `rfh` (range 0.0–336.5085), `rfh_avg` (range 0.0–166.3832), `r1h` (range 0.0–865.9302), `r1h_avg` (range 0.0667–444.788) and 6 others. --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-gnq-rainfall-subnational") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `date` | datetime64[ns] | 0.0% | | | `adm_level` | int64 | 0.0% | 1.0 – 2.0 (mean 1.5) | | `adm_id` | int64 | 0.0% | 1198.0 – 15834.0 (mean 8516.0) | | `pcode` | object | 0.0% | GQ198, GQ199, GQ200 | | `n_pixels` | float64 | 0.0% | 1.0 – 273.0 (mean 124.2143) | | `rfh` | float64 | 0.0% | 0.0 – 336.5085 (mean 57.0921) | | `rfh_avg` | float64 | 0.0% | 0.0 – 166.3832 (mean 57.3784) | | `r1h` | float64 | 0.1% | 0.0 – 865.9302 (mean 171.2694) | | `r1h_avg` | float64 | 0.1% | 0.0667 – 444.788 (mean 172.2305) | | `r3h` | float64 | 0.5% | 0.0 – 1702.5581 (mean 514.0978) | | `r3h_avg` | float64 | 0.5% | 1.8667 – 1081.8893 (mean 517.3441) | | `rfq` | float64 | 0.0% | 8.3589 – 461.8273 (mean 99.9858) | | `r1q` | float64 | 0.1% | 14.0798 – 390.9658 (mean 99.8225) | | `r3q` | float64 | 0.5% | 18.471 – 358.1687 (mean 99.5755) | | `version` | object | 0.0% | final, prelim, forecast | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-07 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `adm_level` | 1.0 | 2.0 | 1.5 | 1.5 | | `adm_id` | 1198.0 | 15834.0 | 8516.0 | 8516.0 | | `n_pixels` | 1.0 | 273.0 | 124.2143 | 100.0 | | `rfh` | 0.0 | 336.5085 | 57.0921 | 48.8592 | | `rfh_avg` | 0.0 | 166.3832 | 57.3784 | 52.8597 | | `r1h` | 0.0 | 865.9302 | 171.2694 | 158.696 | | `r1h_avg` | 0.0667 | 444.788 | 172.2305 | 171.496 | | `r3h` | 0.0 | 1702.5581 | 514.0978 | 504.6784 | | `r3h_avg` | 1.8667 | 1081.8893 | 517.3441 | 510.5557 | | `rfq` | 8.3589 | 461.8273 | 99.9858 | 94.8535 | | `r1q` | 14.0798 | 390.9658 | 99.8225 | 97.4026 | | `r3q` | 18.471 | 358.1687 | 99.5755 | 99.0798 | --- ## 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`. 1 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 WFP - World Food Programme 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/gnq-rainfall-subnational) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_gnq_rainfall_subnational, title = {Equatorial Guinea: Rainfall Indicators at Subnational Level}, author = {WFP - World Food Programme}, year = {2026}, url = {https://data.humdata.org/dataset/gnq-rainfall-subnational}, 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: - 公开获取 language: - 英语 license: cc-by-4.0 multilinguality: - 单语言 size_categories: - 10000 < 样本数 < 100000 source_datasets: - 原创 task_categories: - 表格回归 - 其他 task_ids: [] tags: - 非洲 - 人道主义 - 人道主义数据交换（Humanitarian Data Exchange, HDX） - Electric Sheep Africa - 气候-天气 - 环境 - GNQ pretty_name: "赤道几内亚：次国家级降雨指标" # 赤道几内亚：次国家级降雨指标 **发布方：世界粮食计划署（World Food Programme, WFP）** · **来源：[人道主义数据交换（Humanitarian Data Exchange, HDX）](https://data.humdata.org/dataset/gnq-rainfall-subnational)** · **许可协议：`cc-by`** · **更新时间：2026-04-03** --- ## 摘要 本数据集包含旬度降雨指标，基于气候灾害小组红外降水卫星影像（Climate Hazards Group InfraRed Precipitation, CHIRPS）V2版本结合原位地面站数据，以及CHIRPS-GEFS短期降雨预报数据计算得到，并按次国家级行政单元进行聚合。 包含的指标如下（针对每个旬）： - 10日降雨量 [毫米]（`rfh`） - 1个月滑动聚合降雨量 [毫米]（`r1h`） - 3个月滑动聚合降雨量 [毫米]（`r3h`） - 降雨量长期平均值 [毫米]（`rfh_avg`） - 1个月滑动聚合降雨量长期平均值 [毫米]（`r1h_avg`） - 3个月滑动聚合降雨量长期平均值 [毫米]（`r3h_avg`） - 降雨量距平百分比 [%]（`rfq`） - 1个月降雨量距平百分比 [%]（`r1q`） - 3个月降雨量距平百分比 [%]（`r3q`） 用于聚合的行政单元基于世界粮食计划署（WFP）数据，每个单元均配有Pcode标识。用于生成聚合数据的输入像素数量将通过`n_pixels`列提供。最后，`type`列用于标注该数值基于预报、预发布还是最终产品。 预报于每月6日、16日、26日发布，覆盖即将到来的10天时段（旬），并于每月1日、11日、21日更新为优化版本。预发布观测数据将于每月3日、13日、23日替换上一旬的预报数据，随后最终观测数据将替换预发布数据——于月中（13日或23日）发布，覆盖上月全部三个旬。详情汇总如下： 发布日期：预报类型、覆盖时段（旬） - 1日：更新预报，当月1-10日 - 6日：初始预报，当月11-20日 - 11日：更新预报，当月1-10日 - 16日：初始预报，当月21日至当月月末 - 21日：更新预报，当月11-20日 - 26日：初始预报，下月1-10日 有关CHIRPS-GEFS预报的更多信息，请访问：https://www.chc.ucsb.edu/data/chirps-gefs 如需进一步详情，请参阅方法学部分。 本数据集的每一行均代表时序观测数据。时间覆盖范围由`date`列标注。地理覆盖范围：**GNQ**。 *由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为适合机器学习的Parquet格式。* --- ## 数据集特征 | | | |---|---| | **领域** | 气候与环境 | | **观测单元** | 时序观测数据 | | **总行数** | 22,820 | | **列数** | 17列（12列数值型、4列分类型、1列日期型） | | **训练集划分** | 18,256行 | | **测试集划分** | 4,564行 | | **地理覆盖范围** | GNQ | | **发布方** | 世界粮食计划署（WFP） | | **HDX最后更新时间** | 2026-04-03 | --- ## 变量 **地理变量** — `n_pixels`（取值范围1.0–273.0）。 **时间变量** — `date`。 **标识符/元数据** — `adm_id`（取值范围1198.0–15834.0），`pcode`（GQ198、GQ199、GQ200），`esa_source`（HDX），`esa_processed`（2026-04-07）。 **其他变量** — `adm_level`（取值范围1.0–2.0），`rfh`（取值范围0.0–336.5085），`rfh_avg`（取值范围0.0–166.3832），`r1h`（取值范围0.0–865.9302），`r1h_avg`（取值范围0.0667–444.788），以及另外6个变量。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-gnq-rainfall-subnational") 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 | |---|---|---|---| | `date` | datetime64[ns] | 0.0% | | | `adm_level` | int64 | 0.0% | 1.0 – 2.0（均值1.5） | | `adm_id` | int64 | 0.0% | 1198.0 – 15834.0（均值8516.0） | | `pcode` | object | 0.0% | GQ198、GQ199、GQ200 | | `n_pixels` | float64 | 0.0% | 1.0 – 273.0（均值124.2143） | | `rfh` | float64 | 0.0% | 0.0 – 336.5085（均值57.0921） | | `rfh_avg` | float64 | 0.0% | 0.0 – 166.3832（均值57.3784） | | `r1h` | float64 | 0.1% | 0.0 – 865.9302（均值171.2694） | | `r1h_avg` | float64 | 0.1% | 0.0667 – 444.788（均值172.2305） | | `r3h` | float64 | 0.5% | 0.0 – 1702.5581（均值514.0978） | | `r3h_avg` | float64 | 0.5% | 1.8667 – 1081.8893（均值517.3441） | | `rfq` | float64 | 0.0% | 8.3589 – 461.8273（均值99.9858） | | `r1q` | float64 | 0.1% | 14.0798 – 390.9658（均值99.8225） | | `r3q` | float64 | 0.5% | 18.471 – 358.1687（均值99.5755） | | `version` | object | 0.0% | final、prelim、forecast | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-07 | --- ## 数值统计摘要 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `adm_level` | 1.0 | 2.0 | 1.5 | 1.5 | | `adm_id` | 1198.0 | 15834.0 | 8516.0 | 8516.0 | | `n_pixels` | 1.0 | 273.0 | 124.2143 | 100.0 | | `rfh` | 0.0 | 336.5085 | 57.0921 | 48.8592 | | `rfh_avg` | 0.0 | 166.3832 | 57.3784 | 52.8597 | | `r1h` | 0.0 | 865.9302 | 171.2694 | 158.696 | | `r1h_avg` | 0.0667 | 444.788 | 172.2305 | 171.496 | | `r3h` | 0.0 | 1702.5581 | 514.0978 | 504.6784 | | `r3h_avg` | 1.8667 | 1081.8893 | 517.3441 | 510.5557 | | `rfq` | 8.3589 | 461.8273 | 99.9858 | 94.8535 | | `r1q` | 14.0798 | 390.9658 | 99.8225 | 97.4026 | | `r3q` | 18.471 | 358.1687 | 99.5755 | 99.0798 | --- ## 数据整理流程 原始数据通过CKAN API从HDX下载并转换为Parquet格式。列名均转为小写并标准化为蛇形命名法（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1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 - 数据源自世界粮食计划署（WFP），未由Electric Sheep Africa独立验证。 - 自动化清洗无法修正原始采集过程中错报值、定义不一致或采样偏差问题。 - 请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/gnq-rainfall-subnational)获取发布方提供的方法学说明与注意事项。 --- ## 引用格式 bibtex @dataset{hdx_africa_gnq_rainfall_subnational, title = {Equatorial Guinea: Rainfall Indicators at Subnational Level}, author = {WFP - World Food Programme}, year = {2026}, url = {https://data.humdata.org/dataset/gnq-rainfall-subnational}, note = {Repackaged for machine learning by Electric Sheep Africa (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。*
