electricsheepafrica/africa-mauritania-current-situation-fewsnet-ipc-classification

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- food-security
- mrt
pretty_name: "Mauritania Current Situation FEWS NET Acute Food Insecurity Classifications Data"
dataset_info:
splits:
- name: train
num_examples: 1372
- name: test
num_examples: 343
---

# Mauritania Current Situation FEWS NET Acute Food Insecurity Classifications Data

**Publisher:** FEWS NET · **Source:** [HDX](https://data.humdata.org/dataset/mauritania_current_situation_fewsnet_ipc_classification) · **License:** `cc-by` · **Updated:** 2026-04-01

---

## Abstract

Mauritania Current Situation FEWS NET Acute Food Insecurity Classifications Data from 2011

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: **MRT**.

*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)** | 1,715 |
| **Columns** | 40 (9 numeric, 23 categorical, 7 datetime) |
| **Train split** | 1,372 rows |
| **Test split** | 343 rows |
| **Geographic scope** | MRT |
| **Publisher** | FEWS NET |
| **HDX last updated** | 2026-04-01 |

---

## Variables

**Geographic** — `country` (Mauritania), `country_code` (MR), `fewsnet_region` (West Africa), `unit_type` (fsc_admin_lhz, fsc_admin), `specialization_type` and 2 others.

**Temporal** — `datacollectionperiod` (range 159247.0–159328.0), `reporting_date`.

**Outcome / Measurement** — `value` (range 1.0–3.0).

**Identifier / Metadata** — `source_organization` (FEWS NET), `source_document` (Food Security Outlook, Mauritania), `geographic_unit_full_name` (Zouerate, Tiris Zemmour, Mauritania, Barkewol, Assaba, Mauritania, Bir Moghrein, Tiris Zemmour, Mauritania), `geographic_unit_name` (Agropastoralism, Nomadic pastoralist, Rainfed agriculture), `fnid` (MR2009C11103, MR2009C10301, MR2009C11101) and 8 others.

**Other** — `geographic_group` (Western Africa), `classification_scale`, `is_allowing_for_assistance`, `projection_start`, `projection_end` and 12 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritania-current-situation-fewsnet-ipc-classification")
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 Security Outlook, Mauritania |
| `country` | object | 0.0% | Mauritania |
| `country_code` | object | 0.0% | MR |
| `geographic_group` | object | 0.0% | Western Africa |
| `fewsnet_region` | object | 0.0% | West Africa |
| `geographic_unit_full_name` | object | 0.0% | Zouerate, Tiris Zemmour, Mauritania, Barkewol, Assaba, Mauritania, Bir Moghrein, Tiris Zemmour, Mauritania |
| `geographic_unit_name` | object | 0.0% | Agropastoralism, Nomadic pastoralist, Rainfed agriculture |
| `unit_type` | object | 0.0% | fsc_admin_lhz, fsc_admin |
| `fnid` | object | 0.0% | MR2009C11103, MR2009C10301, MR2009C11101 |
| `classification_scale` | object | 0.0% | |
| `scenario_name` | object | 0.0% | |
| `preference_rating` | int64 | 0.0% | 90.0 – 90.0 (mean 90.0) |
| `is_allowing_for_assistance` | bool | 0.0% | |
| `projection_start` | datetime64[ns] | 0.0% | |
| `projection_end` | datetime64[ns] | 0.0% | |
| `status` | object | 0.0% | |
| `value` | float64 | 0.0% | 1.0 – 3.0 (mean 1.3347) |
| `description` | object | 0.0% | |
| `id` | int64 | 0.0% | 24474855.0 – 24479997.0 (mean 24477426.0) |
| `datacollectionperiod` | int64 | 0.0% | 159247.0 – 159328.0 (mean 159293.4694) |
| `datacollection` | int64 | 0.0% | 168610.0 – 168637.0 (mean 168625.4898) |
| `scenario` | object | 0.0% | |
| `geographic_unit` | int64 | 0.0% | 24509.0 – 24633.0 (mean 24568.9009) |
| `datasourceorganization` | int64 | 0.0% | 1.0 – 1.0 (mean 1.0) |
| `datasourcedocument` | int64 | 0.0% | 6605.0 – 6605.0 (mean 6605.0) |
| `dataseries` | int64 | 0.0% | 6504441.0 – 6505244.0 (mean 6504768.435) |
| `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% | |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `preference_rating` | 90.0 | 90.0 | 90.0 | 90.0 |
| `value` | 1.0 | 3.0 | 1.3347 | 1.0 |
| `id` | 24474855.0 | 24479997.0 | 24477426.0 | 24477426.0 |
| `datacollectionperiod` | 159247.0 | 159328.0 | 159293.4694 | 159298.0 |
| `datacollection` | 168610.0 | 168637.0 | 168625.4898 | 168627.0 |
| `geographic_unit` | 24509.0 | 24633.0 | 24568.9009 | 24568.0 |
| `datasourceorganization` | 1.0 | 1.0 | 1.0 | 1.0 |
| `datasourcedocument` | 6605.0 | 6605.0 | 6605.0 | 6605.0 |
| `dataseries` | 6504441.0 | 6505244.0 | 6504768.435 | 6504729.0 |

---

## 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`. 3 column(s) with >80% missing values were removed: `pct_phase3`, `pct_phase4`, `pct_phase5`. 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/mauritania_current_situation_fewsnet_ipc_classification) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_mauritania_current_situation_fewsnet_ipc_classification,
title = {Mauritania Current Situation FEWS NET Acute Food Insecurity Classifications Data},
author = {FEWS NET},
year = {2026},
url = {https://data.humdata.org/dataset/mauritania_current_situation_fewsnet_ipc_classification},
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: 注释创建者：无注释
language_creators: 语言生成方式：现有资源采集
language: 语言：英语
license: 许可协议：CC BY 4.0
multilinguality: 多语言属性：单语言
size_categories: 数据规模：1000 < 样本数 < 10000
source_datasets: 源数据集：原创数据集
task_categories: 任务类别：表格分类、表格回归
task_ids: 任务子类别：无
tags: 标签：非洲、人道主义、HDX、Electric Sheep Africa、粮食安全、毛里塔尼亚（MRT）
pretty_name: "毛里塔尼亚当前局势FEWS NET急性粮食不安全分类数据"
dataset_info:
splits:
- name: 训练集
num_examples: 1372
- name: 测试集
num_examples: 343

# 毛里塔尼亚当前局势FEWS NET急性粮食不安全分类数据
**发布方：** 全球粮食安全预警系统网络（Famine Early Warning Systems Network, FEWS NET） · **来源：** [人道主义数据交换中心（Humanitarian Data Exchange, HDX）](https://data.humdata.org/dataset/mauritania_current_situation_fewsnet_ipc_classification) · **许可协议：** `cc-by` · **更新时间：** 2026-04-01

---

## 摘要
2011年起的毛里塔尼亚当前局势FEWS NET急性粮食不安全分类数据。
本数据集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`、`projection_end`字段标识。地理范围：**毛里塔尼亚（MRT）**。
*本数据集由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为机器学习可用的Parquet格式。*

---

## 数据集特征
| | |
|---|---|
| **领域** | 粮食安全与营养 |
| **观测单元** | 一级行政单元观测记录 |
| **总行数** | 1,715 |
| **字段数** | 40（9个数值型、23个分类型、7个日期时间型） |
| **训练集划分** | 1,372条 |
| **测试集划分** | 343条 |
| **地理范围** | 毛里塔尼亚（MRT） |
| **发布方** | 全球粮食安全预警系统网络（FEWS NET） |
| **HDX最后更新时间** | 2026-04-01 |

---

## 字段分类
**地理类字段**：`country`（毛里塔尼亚）、`country_code`（MR）、`fewsnet_region`（西非）、`unit_type`（fsc_admin_lhz、fsc_admin）、`specialization_type`及另外2个字段。
**时间类字段**：`datacollectionperiod`（取值范围159247.0–159328.0）、`reporting_date`。
**结果/测量类字段**：`value`（取值范围1.0–3.0）。
**标识符/元数据字段**：`source_organization`（FEWS NET）、`source_document`（《毛里塔尼亚粮食安全展望》）、`geographic_unit_full_name`（祖埃拉特、提里斯-宰穆尔大区、毛里塔尼亚；巴尔科勒、阿萨巴大区、毛里塔尼亚；比尔莫格兰、提里斯-宰穆尔大区、毛里塔尼亚等）、`geographic_unit_name`（农牧业、游牧畜牧业、雨养农业）、`fnid`（MR2009C11103、MR2009C10301、MR2009C11101等）及另外8个字段。
**其他字段**：`geographic_group`（西非）、`classification_scale`、`is_allowing_for_assistance`、`projection_start`、`projection_end`及另外12个字段。

---

## 快速上手
python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritania-current-situation-fewsnet-ipc-classification")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()

---

## 字段 Schema
| 字段名 | 数据类型 | 缺失率 | 取值范围/示例值 |
|---|---|---|---|
| `source_organization` | object | 0.0% | FEWS NET |
| `source_document` | object | 0.0% | 《毛里塔尼亚粮食安全展望》 |
| `country` | object | 0.0% | 毛里塔尼亚 |
| `country_code` | object | 0.0% | MR |
| `geographic_group` | object | 0.0% | 西非 |
| `fewsnet_region` | object | 0.0% | 西非 |
| `geographic_unit_full_name` | object | 0.0% | 祖埃拉特、提里斯-宰穆尔大区、毛里塔尼亚；巴尔科勒、阿萨巴大区、毛里塔尼亚；比尔莫格兰、提里斯-宰穆尔大区、毛里塔尼亚等 |
| `geographic_unit_name` | object | 0.0% | 农牧业、游牧畜牧业、雨养农业 |
| `unit_type` | object | 0.0% | fsc_admin_lhz、fsc_admin |
| `fnid` | object | 0.0% | MR2009C11103、MR2009C10301、MR2009C11101等 |
| `classification_scale` | object | 0.0% | 无 |
| `scenario_name` | object | 0.0% | 无 |
| `preference_rating` | int64 | 0.0% | 90.0 – 90.0（均值90.0） |
| `is_allowing_for_assistance` | bool | 0.0% | 无 |
| `projection_start` | datetime64[ns] | 0.0% | 无 |
| `projection_end` | datetime64[ns] | 0.0% | 无 |
| `status` | object | 0.0% | 无 |
| `value` | float64 | 0.0% | 1.0 – 3.0（均值1.3347） |
| `description` | object | 0.0% | 无 |
| `id` | int64 | 0.0% | 24474855.0 – 24479997.0（均值24477426.0） |
| `datacollectionperiod` | int64 | 0.0% | 159247.0 – 159328.0（均值159293.4694） |
| `datacollection` | int64 | 0.0% | 168610.0 – 168637.0（均值168625.4898） |
| `scenario` | object | 0.0% | 无 |
| `geographic_unit` | int64 | 0.0% | 24509.0 – 24633.0（均值24568.9009） |
| `datasourceorganization` | int64 | 0.0% | 1.0 – 1.0（均值1.0） |
| `datasourcedocument` | int64 | 0.0% | 6605.0 – 6605.0（均值6605.0） |
| `dataseries` | int64 | 0.0% | 6504441.0 – 6505244.0（均值6504768.435） |
| `dataseries_name` | object | 0.0% | 无 |
| `specialization_type` | object | 0.0% | 无 |
| `dataseries_specialization_type` | object | 0.0% | 无 |
| `data_usage_policy` | object | 0.0% | 无 |
| `created` | datetime64[ns] | 0.0% | 无 |
| `modified` | datetime64[ns] | 0.0% | 无 |
| `status_changed` | datetime64[ns] | 0.0% | 无 |
| `collection_status` | object | 0.0% | 无 |
| `collection_status_changed` | datetime64[ns] | 0.0% | 无 |
| `collection_schedule` | object | 0.0% | 无 |
| `reporting_date` | datetime64[ns] | 0.0% | 无 |
| `esa_source` | object | 0.0% | 无 |
| `esa_processed` | object | 0.0% | 无 |

---

## 数值型字段统计
| 字段名 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|---|
| `preference_rating` | 90.0 | 90.0 | 90.0 | 90.0 |
| `value` | 1.0 | 3.0 | 1.3347 | 1.0 |
| `id` | 24474855.0 | 24479997.0 | 24477426.0 | 24477426.0 |
| `datacollectionperiod` | 159247.0 | 159328.0 | 159293.4694 | 159298.0 |
| `datacollection` | 168610.0 | 168637.0 | 168625.4898 | 168627.0 |
| `geographic_unit` | 24509.0 | 24633.0 | 24568.9009 | 24568.0 |
| `datasourceorganization` | 1.0 | 1.0 | 1.0 | 1.0 |
| `datasourcedocument` | 6605.0 | 6605.0 | 6605.0 | 6605.0 |
| `dataseries` | 6504441.0 | 6505244.0 | 6504768.435 | 6504729.0 |

---

## 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。字段名统一转换为小写蛇形命名法。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3个缺失率超过80%的字段：`pct_phase3`、`pct_phase4`、`pct_phase5`。基于解析成功率（阈值>85%），将7个字段从字符串类型转换为数值型或日期时间型。采用固定随机种子（42）将数据集按80/20比例划分为训练集与测试集，并以Snappy压缩格式保存为Parquet文件。

---

## 局限性说明
- 数据源自FEWS NET，未经过Electric Sheep Africa的独立验证。
- 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。
- 请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/mauritania_current_situation_fewsnet_ipc_classification)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_mauritania_current_situation_fewsnet_ipc_classification,
title = {毛里塔尼亚当前局势FEWS NET急性粮食不安全分类数据},
author = {FEWS NET},
year = {2026},
url = {https://data.humdata.org/dataset/mauritania_current_situation_fewsnet_ipc_classification},
note = {由Electric Sheep Africa（https://huggingface.co/electricsheepafrica）重新打包以适配机器学习场景}
}

---

*[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。*