electricsheepafrica/africa-zimbabwe-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 - zwe pretty_name: "Zimbabwe Most Likely FEWS NET Acutely Food Insecure Population Estimates Data" dataset_info: splits: - name: train num_examples: 60 - name: test num_examples: 15 --- # Zimbabwe Most Likely FEWS NET Acutely Food Insecure Population Estimates Data **Publisher:** FEWS NET · **Source:** [HDX](https://data.humdata.org/dataset/zimbabwe_most_likely_fewsnet_fipe) · **License:** `cc-by` · **Updated:** 2026-03-30 --- ## Abstract Zimbabwe 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: **ZWE**. *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** | ZWE | | **Publisher** | FEWS NET | | **HDX last updated** | 2026-03-30 | --- ## Variables **Geographic** — `country` (Zimbabwe), `country_code` (ZW), `fewsnet_region` (Southern Africa), `admin_0` (Zimbabwe), `specialization_type` and 3 others. **Temporal** — `datacollectionperiod` (range 310323.0–373073.0), `reporting_date`. **Outcome / Measurement** — `phase`, `low_value` (range 500000.0–5000000.0), `high_value` (range 999999.0–7499999.0), `value` (range 500000.0–5000000.0), `phase_name`. **Identifier / Metadata** — `source_organization` (FEWS NET), `source_document` (Food Assistance Outlook Brief), `geographic_unit_full_name` (Zimbabwe), `geographic_unit_name` (Zimbabwe), `fnid` (ZW) and 8 others. **Other** — `geographic_group` (Southern Africa), `indicator_abbreviation`, `projection_start`, `projection_end`, `status` and 11 others. --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-zimbabwe-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% | Zimbabwe | | `country_code` | object | 0.0% | ZW | | `geographic_group` | object | 0.0% | Southern Africa | | `fewsnet_region` | object | 0.0% | Southern Africa | | `geographic_unit_full_name` | object | 0.0% | Zimbabwe | | `geographic_unit_name` | object | 0.0% | Zimbabwe | | `fnid` | object | 0.0% | ZW | | `admin_0` | object | 0.0% | Zimbabwe | | `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% | 500000.0 – 5000000.0 (mean 2010000.0) | | `high_value` | float64 | 0.0% | 999999.0 – 7499999.0 (mean 3426665.6667) | | `value` | float64 | 0.0% | 500000.0 – 5000000.0 (mean 2010000.0) | | `id` | int64 | 0.0% | 33126792.0 – 40657285.0 (mean 34149657.7333) | | `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% | 8218.0 – 8218.0 (mean 8218.0) | | `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% | 6932837.0 – 6932837.0 (mean 6932837.0) | | `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` | 500000.0 | 5000000.0 | 2010000.0 | 2000000.0 | | `high_value` | 999999.0 | 7499999.0 | 3426665.6667 | 2499999.0 | | `value` | 500000.0 | 5000000.0 | 2010000.0 | 2000000.0 | | `id` | 33126792.0 | 40657285.0 | 34149657.7333 | 33128904.0 | | `datacollectionperiod` | 310323.0 | 373073.0 | 319280.3333 | 310397.0 | | `datacollection` | 325936.0 | 383437.0 | 333815.6 | 325973.0 | | `geographic_unit` | 8218.0 | 8218.0 | 8218.0 | 8218.0 | | `datasourceorganization` | 1.0 | 1.0 | 1.0 | 1.0 | | `datasourcedocument` | 6986.0 | 6986.0 | 6986.0 | 6986.0 | | `dataseries` | 6932837.0 | 6932837.0 | 6932837.0 | 6932837.0 | --- ## 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/zimbabwe_most_likely_fewsnet_fipe) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_zimbabwe_most_likely_fewsnet_fipe, title = {Zimbabwe Most Likely FEWS NET Acutely Food Insecure Population Estimates Data}, author = {FEWS NET}, year = {2026}, url = {https://data.humdata.org/dataset/zimbabwe_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: - 爬取获取 language: - 英语 license: CC-BY-4.0 multilinguality: - 单语言 size_categories: - 样本量小于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 - Electric Sheep Africa - 粮食安全 - ZWE pretty_name: "津巴布韦FEWS NET急性粮食不安全人口最可能估算数据" dataset_info: splits: - name: 训练集 num_examples: 60 - name: 测试集 num_examples: 15 # 津巴布韦FEWS NET急性粮食不安全人口最可能估算数据集 **发布方：FEWS NET（饥荒预警系统网络）** · **来源：[HDX（人道主义数据交换）](https://data.humdata.org/dataset/zimbabwe_most_likely_fewsnet_fipe)** · **许可协议：`CC-BY`** · **更新时间：2026-03-30** --- ## 摘要 津巴布韦FEWS NET急性粮食不安全人口最可能估算数据（2019年） 本数据集的每一行对应一级行政单元的观测记录。时间覆盖范围由`projection_start`、`projection_end`两列标注。地理覆盖范围：**ZWE（津巴布韦国家代码）**。 *本数据集由[Electric Sheep Africa（电羊非洲）](https://huggingface.co/electricsheepafrica)整理为适配机器学习的Parquet格式。* --- ## 数据集特征 | | | |---|---| | **领域** | 粮食安全与营养 | | **观测单元** | 一级行政单元观测值 | | **总行数** | 75 | | **列数** | 44（10个数值型、27个分类型、7个日期型） | | **训练集划分** | 60行 | | **测试集划分** | 15行 | | **地理覆盖范围** | ZWE | | **发布方** | FEWS NET | | **HDX最后更新时间** | 2026-03-30 | --- ## 变量分类 **地理类变量**：`country`（津巴布韦）、`country_code`（ZW）、`fewsnet_region`（南部非洲）、`admin_0`（津巴布韦）、`specialization_type`及其他3个变量。 **时间类变量**：`datacollectionperiod`（取值范围310323.0–373073.0）、`reporting_date`。 **结果/测量变量**：`phase`、`low_value`（取值范围500000.0–5000000.0）、`high_value`（取值范围999999.0–7499999.0）、`value`（取值范围500000.0–5000000.0）、`phase_name`。 **标识符/元数据变量**：`source_organization`（FEWS NET）、`source_document`（《粮食援助展望简报》）、`geographic_unit_full_name`（津巴布韦）、`geographic_unit_name`（津巴布韦）、`fnid`（ZW）及其他8个变量。 **其他变量**：`geographic_group`（南部非洲）、`indicator_abbreviation`、`projection_start`、`projection_end`、`status`及其他11个变量。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-zimbabwe-most-likely-fewsnet-fipe") 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 | |---|---|---|---| | `source_organization` | 字符型 | 0.0% | FEWS NET | | `source_document` | 字符型 | 0.0% | 《粮食援助展望简报》 | | `country` | 字符型 | 0.0% | 津巴布韦 | | `country_code` | 字符型 | 0.0% | ZW | | `geographic_group` | 字符型 | 0.0% | 南部非洲 | | `fewsnet_region` | 字符型 | 0.0% | 南部非洲 | | `geographic_unit_full_name` | 字符型 | 0.0% | 津巴布韦 | | `geographic_unit_name` | 字符型 | 0.0% | 津巴布韦 | | `fnid` | 字符型 | 0.0% | ZW | | `admin_0` | 字符型 | 0.0% | 津巴布韦 | | `phase` | 字符型 | 0.0% | 无 | | `scenario_name` | 字符型 | 0.0% | 无 | | `indicator_name` | 字符型 | 0.0% | 无 | | `indicator_abbreviation` | 字符型 | 0.0% | 无 | | `projection_start` | datetime64[ns] | 0.0% | 无 | | `projection_end` | datetime64[ns] | 0.0% | 无 | | `status` | 字符型 | 0.0% | 无 | | `low_value` | float64 | 0.0% | 500000.0 – 5000000.0（均值2010000.0） | | `high_value` | float64 | 0.0% | 999999.0 – 7499999.0（均值3426665.6667） | | `value` | float64 | 0.0% | 500000.0 – 5000000.0（均值2010000.0） | | `id` | int64 | 0.0% | 33126792.0 – 40657285.0（均值34149657.7333） | | `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` | 字符型 | 0.0% | 无 | | `geographic_unit` | int64 | 0.0% | 8218.0 – 8218.0（均值8218.0） | | `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% | 6932837.0 – 6932837.0（均值6932837.0） | | `dataseries_name` | 字符型 | 0.0% | 无 | | `specialization_type` | 字符型 | 0.0% | 无 | | `dataseries_specialization_type` | 字符型 | 0.0% | 无 | | `data_usage_policy` | 字符型 | 0.0% | 无 | | `created` | datetime64[ns] | 0.0% | 无 | | `modified` | datetime64[ns] | 0.0% | 无 | | `status_changed` | datetime64[ns] | 0.0% | 无 | | `collection_status` | 字符型 | 0.0% | 无 | | `collection_status_changed` | datetime64[ns] | 0.0% | 无 | | `collection_schedule` | 字符型 | 0.0% | 无 | | `reporting_date` | datetime64[ns] | 0.0% | 无 | | `phase_name` | 字符型 | 0.0% | 无 | | `population_range` | 字符型 | 0.0% | 无 | | `description` | 字符型 | 0.0% | 无 | | `esa_source` | 字符型 | 0.0% | 无 | | `esa_processed` | 字符型 | 0.0% | 无 | --- ## 数值型变量统计摘要 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `low_value` | 500000.0 | 5000000.0 | 2010000.0 | 2000000.0 | | `high_value` | 999999.0 | 7499999.0 | 3426665.6667 | 2499999.0 | | `value` | 500000.0 | 5000000.0 | 2010000.0 | 2000000.0 | | `id` | 33126792.0 | 40657285.0 | 34149657.7333 | 33128904.0 | | `datacollectionperiod` | 310323.0 | 373073.0 | 319280.3333 | 310397.0 | | `datacollection` | 325936.0 | 383437.0 | 333815.6 | 325973.0 | | `geographic_unit` | 8218.0 | 8218.0 | 8218.0 | 8218.0 | | `datasourceorganization` | 1.0 | 1.0 | 1.0 | 1.0 | | `datasourcedocument` | 6986.0 | 6986.0 | 6986.0 | 6986.0 | | `dataseries` | 6932837.0 | 6932837.0 | 6932837.0 | 6932837.0 | --- ## 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。所有列名均转为小写并标准化为蛇形命名法（snake_case）。将常见缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）统一替换为`NaN`。删除了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85%），将7列从字符串类型转换为数值型或日期型。使用固定随机种子（42）将数据集按80/20划分为训练集与测试集，并保存为Snappy压缩的Parquet格式。 --- ## 数据集局限性 1. 数据源自FEWS NET，未经过欧洲空间局（ESA）的独立验证。 2. 自动化清洗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。 3. 请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/zimbabwe_most_likely_fewsnet_fipe)以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_most_likely_fewsnet_fipe, title = {津巴布韦FEWS NET急性粮食不安全人口最可能估算数据}, author = {FEWS NET}, year = {2026}, url = {https://data.humdata.org/dataset/zimbabwe_most_likely_fewsnet_fipe}, note = {由Electric Sheep Africa（电羊非洲）重新打包以适配机器学习需求，来源：https://huggingface.co/electricsheepafrica} } --- *[Electric Sheep Africa（电羊非洲）](https://huggingface.co/electricsheepafrica) — 非洲机器学习数据集基础设施提供商，尼日利亚拉各斯。*