electricsheepafrica/africa-nigeria-most-likely-fewsnet-fipe

--- 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 - food-security - nga pretty_name: "Nigeria Most Likely FEWS NET Acutely Food Insecure Population Estimates Data" dataset_info: splits: - name: train num_examples: 60 - name: test num_examples: 15 --- # Nigeria Most Likely FEWS NET Acutely Food Insecure Population Estimates Data **Publisher:** FEWS NET · **Source:** [HDX](https://data.humdata.org/dataset/nigeria_most_likely_fewsnet_fipe) · **License:** `cc-by` · **Updated:** 2026-04-01 --- ## Abstract Nigeria Most Likely FEWS NET Acutely Food Insecure Population Estimates Data from 2019 Each row in this dataset represents first-level administrative unit observations. Temporal coverage is indicated by the `projection_start`, `projection_end` column(s). Geographic scope: **NGA**. *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).* --- ## Dataset Characteristics | | | |---|---| | **Domain** | Food security and nutrition | | **Unit of observation** | First-level administrative unit observations | | **Rows (total)** | 75 | | **Columns** | 44 (10 numeric, 27 categorical, 7 datetime) | | **Train split** | 60 rows | | **Test split** | 15 rows | | **Geographic scope** | NGA | | **Publisher** | FEWS NET | | **HDX last updated** | 2026-04-01 | --- ## Variables **Geographic** — `country` (Nigeria), `country_code` (NG), `fewsnet_region` (West Africa), `admin_0` (Nigeria), `specialization_type` and 3 others. **Temporal** — `datacollectionperiod` (range 310323.0–373073.0), `reporting_date`. **Outcome / Measurement** — `phase`, `low_value` (range 2500000.0–21000000.0), `high_value` (range 4999999.0–21999999.0), `value` (range 2500000.0–21000000.0), `phase_name`. **Identifier / Metadata** — `source_organization` (FEWS NET), `source_document` (Food Assistance Outlook Brief), `geographic_unit_full_name` (Nigeria), `geographic_unit_name` (Nigeria), `fnid` (NG) and 8 others. **Other** — `geographic_group` (Western Africa), `indicator_abbreviation`, `projection_start`, `projection_end`, `status` and 11 others. --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-nigeria-most-likely-fewsnet-fipe") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `source_organization` | object | 0.0% | FEWS NET | | `source_document` | object | 0.0% | Food Assistance Outlook Brief | | `country` | object | 0.0% | Nigeria | | `country_code` | object | 0.0% | NG | | `geographic_group` | object | 0.0% | Western Africa | | `fewsnet_region` | object | 0.0% | West Africa | | `geographic_unit_full_name` | object | 0.0% | Nigeria | | `geographic_unit_name` | object | 0.0% | Nigeria | | `fnid` | object | 0.0% | NG | | `admin_0` | object | 0.0% | Nigeria | | `phase` | object | 0.0% | | | `scenario_name` | object | 0.0% | | | `indicator_name` | object | 0.0% | | | `indicator_abbreviation` | object | 0.0% | | | `projection_start` | datetime64[ns] | 0.0% | | | `projection_end` | datetime64[ns] | 0.0% | | | `status` | object | 0.0% | | | `low_value` | float64 | 0.0% | 2500000.0 – 21000000.0 (mean 7986666.6667) | | `high_value` | float64 | 0.0% | 4999999.0 – 21999999.0 (mean 10306665.6667) | | `value` | float64 | 0.0% | 2500000.0 – 21000000.0 (mean 7986666.6667) | | `id` | int64 | 0.0% | 33126772.0 – 40657273.0 (mean 34149636.2667) | | `datacollectionperiod` | int64 | 0.0% | 310323.0 – 373073.0 (mean 319280.3333) | | `datacollection` | int64 | 0.0% | 325936.0 – 383437.0 (mean 333815.6) | | `scenario` | object | 0.0% | | | `geographic_unit` | int64 | 0.0% | 8133.0 – 8133.0 (mean 8133.0) | | `datasourceorganization` | int64 | 0.0% | 1.0 – 1.0 (mean 1.0) | | `datasourcedocument` | int64 | 0.0% | 6986.0 – 6986.0 (mean 6986.0) | | `dataseries` | int64 | 0.0% | 6932817.0 – 6932817.0 (mean 6932817.0) | | `dataseries_name` | object | 0.0% | | | `specialization_type` | object | 0.0% | | | `dataseries_specialization_type` | object | 0.0% | | | `data_usage_policy` | object | 0.0% | | | `created` | datetime64[ns] | 0.0% | | | `modified` | datetime64[ns] | 0.0% | | | `status_changed` | datetime64[ns] | 0.0% | | | `collection_status` | object | 0.0% | | | `collection_status_changed` | datetime64[ns] | 0.0% | | | `collection_schedule` | object | 0.0% | | | `reporting_date` | datetime64[ns] | 0.0% | | | `phase_name` | object | 0.0% | | | `population_range` | object | 0.0% | | | `description` | object | 0.0% | | | `esa_source` | object | 0.0% | | | `esa_processed` | object | 0.0% | | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `low_value` | 2500000.0 | 21000000.0 | 7986666.6667 | 7500000.0 | | `high_value` | 4999999.0 | 21999999.0 | 10306665.6667 | 9999999.0 | | `value` | 2500000.0 | 21000000.0 | 7986666.6667 | 7500000.0 | | `id` | 33126772.0 | 40657273.0 | 34149636.2667 | 33128884.0 | | `datacollectionperiod` | 310323.0 | 373073.0 | 319280.3333 | 310397.0 | | `datacollection` | 325936.0 | 383437.0 | 333815.6 | 325973.0 | | `geographic_unit` | 8133.0 | 8133.0 | 8133.0 | 8133.0 | | `datasourceorganization` | 1.0 | 1.0 | 1.0 | 1.0 | | `datasourcedocument` | 6986.0 | 6986.0 | 6986.0 | 6986.0 | | `dataseries` | 6932817.0 | 6932817.0 | 6932817.0 | 6932817.0 | --- ## 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`. 7 column(s) with >80% missing values were removed: `admin_1`, `admin_2`, `admin_3`, `admin_4`, `pct_phase3`, `pct_phase4`.... 7 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 FEWS NET 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/nigeria_most_likely_fewsnet_fipe) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_nigeria_most_likely_fewsnet_fipe, title = {Nigeria Most Likely FEWS NET Acutely Food Insecure Population Estimates Data}, author = {FEWS NET}, year = {2026}, url = {https://data.humdata.org/dataset/nigeria_most_likely_fewsnet_fipe}, 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: - 无标注 language_creators: - 现有公开资源抓取 language: - 英语 license: cc-by-4.0 multilinguality: - 单语言 size_categories: - 少于1000条 source_datasets: - 原始数据集 task_categories: - 表格分类 - 其他 task_ids: [] tags: - 非洲 - 人道主义 - HDX（Humanitarian Data Exchange，人道主义数据交换） - Electric Sheep Africa - 粮食安全 - NGA（尼日利亚国家代码） pretty_name: "尼日利亚最可能FEWS NET（饥荒早期预警系统网络，Famine Early Warning Systems Network）急性粮食不安全人口估算数据" dataset_info: splits: - name: 训练集 num_examples: 60 - name: 测试集 num_examples: 15 # 尼日利亚最可能FEWS NET急性粮食不安全人口估算数据 **发布方**：FEWS NET（饥荒早期预警系统网络，Famine Early Warning Systems Network） · **来源**：[HDX（人道主义数据交换，Humanitarian Data Exchange）](https://data.humdata.org/dataset/nigeria_most_likely_fewsnet_fipe) · **授权协议**：`CC BY` · **更新时间**：2026-04-01 --- ## 摘要 本数据集为2019年尼日利亚最可能FEWS NET急性粮食不安全人口估算数据。 数据集中每一行代表一级行政单元的观测记录，时间覆盖范围由`projection_start`（预测开始时间）和`projection_end`（预测结束时间）两列标注。地理覆盖范围：**NGA（尼日利亚国家代码）**。 *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 | 指标 | 详情 | |---|---| | **领域** | 粮食安全与营养 | | **观测单元** | 一级行政单元 | | **总样本行数** | 75 | | **总列数** | 44列（10列数值型、27列分类型、7列日期时间型） | | **训练集样本数** | 60行 | | **测试集样本数** | 15行 | | **地理覆盖范围** | NGA（尼日利亚国家代码） | | **发布方** | FEWS NET | | **HDX最后更新时间** | 2026-04-01 | --- ## 变量说明 **地理类变量**：`country`（国家：尼日利亚）、`country_code`（国家代码：NG）、`fewsnet_region`（FEWS NET区域：西非）、`admin_0`（行政层级0：尼日利亚）、`specialization_type`（专业类型）及另外3个变量。 **时间类变量**：`datacollectionperiod`（数据收集周期，取值范围310323.0–373073.0）、`reporting_date`（报告日期）。 **结果/测量类变量**：`phase`（阶段）、`low_value`（下限值，取值范围2500000.0–21000000.0）、`high_value`（上限值，取值范围4999999.0–21999999.0）、`value`（估算值，取值范围2500000.0–21000000.0）、`phase_name`（阶段名称）。 **标识/元数据类变量**：`source_organization`（来源机构：FEWS NET）、`source_document`（来源文档：粮食援助展望简报，Food Assistance Outlook Brief）、`geographic_unit_full_name`（地理单元全称：尼日利亚）、`geographic_unit_name`（地理单元名称：尼日利亚）、`fnid`（FNID代码：NG）及另外8个变量。 **其他类变量**：`geographic_group`（地理群组：西非）、`indicator_abbreviation`（指标缩写）、`projection_start`（预测开始时间）、`projection_end`（预测结束时间）、`status`（状态）及另外11个变量。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-nigeria-most-likely-fewsnet-fipe") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() --- ## 数据结构 | 字段名 | 数据类型 | 空值占比 | 取值范围/示例值 | |---|---|---|---| | `source_organization` | object | 0.0% | FEWS NET | | `source_document` | object | 0.0% | 粮食援助展望简报（Food Assistance Outlook Brief） | | `country` | object | 0.0% | 尼日利亚 | | `country_code` | object | 0.0% | NG | | `geographic_group` | object | 0.0% | 西非（Western Africa） | | `fewsnet_region` | object | 0.0% | 西非（West Africa） | | `geographic_unit_full_name` | object | 0.0% | 尼日利亚 | | `geographic_unit_name` | object | 0.0% | 尼日利亚 | | `fnid` | object | 0.0% | NG | | `admin_0` | object | 0.0% | 尼日利亚 | | `phase` | object | 0.0% | - | | `scenario_name` | object | 0.0% | - | | `indicator_name` | object | 0.0% | - | | `indicator_abbreviation` | object | 0.0% | - | | `projection_start` | datetime64[ns] | 0.0% | - | | `projection_end` | datetime64[ns] | 0.0% | - | | `status` | object | 0.0% | - | | `low_value` | float64 | 0.0% | 2500000.0 – 21000000.0（均值：7986666.6667） | | `high_value` | float64 | 0.0% | 4999999.0 – 21999999.0（均值：10306665.6667） | | `value` | float64 | 0.0% | 2500000.0 – 21000000.0（均值：7986666.6667） | | `id` | int64 | 0.0% | 33126772.0 – 40657273.0（均值：34149636.2667） | | `datacollectionperiod` | int64 | 0.0% | 310323.0 – 373073.0（均值：319280.3333） | | `datacollection` | int64 | 0.0% | 325936.0 – 383437.0（均值：333815.6） | | `scenario` | object | 0.0% | - | | `geographic_unit` | int64 | 0.0% | 8133.0 – 8133.0（均值：8133.0） | | `datasourceorganization` | int64 | 0.0% | 1.0 – 1.0（均值：1.0） | | `datasourcedocument` | int64 | 0.0% | 6986.0 – 6986.0（均值：6986.0） | | `dataseries` | int64 | 0.0% | 6932817.0 – 6932817.0（均值：6932817.0） | | `dataseries_name` | object | 0.0% | - | | `specialization_type` | object | 0.0% | - | | `dataseries_specialization_type` | object | 0.0% | - | | `data_usage_policy` | object | 0.0% | - | | `created` | datetime64[ns] | 0.0% | - | | `modified` | datetime64[ns] | 0.0% | - | | `status_changed` | datetime64[ns] | 0.0% | - | | `collection_status` | object | 0.0% | - | | `collection_status_changed` | datetime64[ns] | 0.0% | - | | `collection_schedule` | object | 0.0% | - | | `reporting_date` | datetime64[ns] | 0.0% | - | | `phase_name` | object | 0.0% | - | | `population_range` | object | 0.0% | - | | `description` | object | 0.0% | - | | `esa_source` | object | 0.0% | - | | `esa_processed` | object | 0.0% | - | --- ## 数值型变量统计摘要 | 字段名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `low_value` | 2500000.0 | 21000000.0 | 7986666.6667 | 7500000.0 | | `high_value` | 4999999.0 | 21999999.0 | 10306665.6667 | 9999999.0 | | `value` | 2500000.0 | 21000000.0 | 7986666.6667 | 7500000.0 | | `id` | 33126772.0 | 40657273.0 | 34149636.2667 | 33128884.0 | | `datacollectionperiod` | 310323.0 | 373073.0 | 319280.3333 | 310397.0 | | `datacollection` | 325936.0 | 383437.0 | 333815.6 | 325973.0 | | `geographic_unit` | 8133.0 | 8133.0 | 8133.0 | 8133.0 | | `datasourceorganization` | 1.0 | 1.0 | 1.0 | 1.0 | | `datasourcedocument` | 6986.0 | 6986.0 | 6986.0 | 6986.0 | | `dataseries` | 6932817.0 | 6932817.0 | 6932817.0 | 6932817.0 | --- ## 数据整理流程 原始数据通过CKAN API从HDX（人道主义数据交换）下载，并转换为Parquet格式。所有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`。移除了7个缺失值占比超过80%的列：`admin_1`、`admin_2`、`admin_3`、`admin_4`、`pct_phase3`、`pct_phase4`等。基于解析成功率（阈值为85%），将7列从字符串类型转换为数值型或日期时间型。数据集以80:20的比例划分为训练集与测试集，使用固定随机种子（42）进行划分，并保存为Snappy压缩的Parquet格式。 --- ## 数据集局限性 - 本数据集源自FEWS NET，尚未由Electric Sheep Africa进行独立验证。 - 自动化数据清洗无法修正原始数据收集中的错报值、定义不一致问题或抽样偏差。 - 如需了解发布方的方法说明与免责声明，请参阅[HDX原始数据集页面](https://data.humdata.org/dataset/nigeria_most_likely_fewsnet_fipe)。 --- ## 引用格式 bibtex @dataset{hdx_africa_nigeria_most_likely_fewsnet_fipe, title = {尼日利亚最可能FEWS NET急性粮食不安全人口估算数据}, author = {FEWS NET}, year = {2026}, url = {https://data.humdata.org/dataset/nigeria_most_likely_fewsnet_fipe}, note = {由Electric Sheep Africa重新打包以适配机器学习场景 (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。*