electricsheepafrica/africa-sierra-leone-current-situation-fewsnet-ipc-classification

---
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
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- food-security
- sle
pretty_name: "Sierra Leone Current Situation FEWS NET Acute Food Insecurity Classifications Data"
dataset_info:
splits:
- name: train
num_examples: 85
- name: test
num_examples: 21
---

# Sierra Leone Current Situation FEWS NET Acute Food Insecurity Classifications Data

**Publisher:** FEWS NET · **Source:** [HDX](https://data.humdata.org/dataset/sierra_leone_current_situation_fewsnet_ipc_classification) · **License:** `cc-by` · **Updated:** 2026-04-07

---

## Abstract

Sierra Leone Current Situation FEWS NET Acute Food Insecurity Classifications Data from 2015

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: **SLE**.

*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)** | 107 |
| **Columns** | 40 (9 numeric, 23 categorical, 7 datetime) |
| **Train split** | 85 rows |
| **Test split** | 21 rows |
| **Geographic scope** | SLE |
| **Publisher** | FEWS NET |
| **HDX last updated** | 2026-04-07 |

---

## Variables

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

**Temporal** — `datacollectionperiod` (range 158983.0–158992.0), `reporting_date`.

**Outcome / Measurement** — `value` (range 1.0–2.0).

**Identifier / Metadata** — `source_organization` (FEWS NET, Sierra Leone), `source_document` (Food Security Outlook, Sierra Leone), `geographic_unit_full_name` (Bombali Food Crops, Peppers, Tobacco and Livestock, Bombali, Northern, Sierra Leone, Koinadugu Livestock, Food Crops and Trade, Kono, Eastern, Sierra Leone, Kono-Kenema-Bo Rice, Tree Crops and Timber, Kono, Eastern, Sierra Leone), `geographic_unit_name` (Western Rice, Root Crops, Cereals and Trade Belt, Rice Bowl Areas, Coastal Food Crops and Fishing), `fnid` (SL2016C3020102, SL2016C3010307, SL2016C3010306) 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-sierra-leone-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, Sierra Leone |
| `source_document` | object | 0.0% | Food Security Outlook, Sierra Leone |
| `country` | object | 0.0% | Sierra Leone |
| `country_code` | object | 0.0% | SL |
| `geographic_group` | object | 0.0% | Western Africa |
| `fewsnet_region` | object | 0.0% | West Africa |
| `geographic_unit_full_name` | object | 0.0% | Bombali Food Crops, Peppers, Tobacco and Livestock, Bombali, Northern, Sierra Leone, Koinadugu Livestock, Food Crops and Trade, Kono, Eastern, Sierra Leone, Kono-Kenema-Bo Rice, Tree Crops and Timber, Kono, Eastern, Sierra Leone |
| `geographic_unit_name` | object | 0.0% | Western Rice, Root Crops, Cereals and Trade Belt, Rice Bowl Areas, Coastal Food Crops and Fishing |
| `unit_type` | object | 0.0% | fsc_admin_lhz, fsc_admin |
| `fnid` | object | 0.0% | SL2016C3020102, SL2016C3010307, SL2016C3010306 |
| `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 – 2.0 (mean 1.6449) |
| `description` | object | 0.0% | |
| `id` | int64 | 0.0% | 24448077.0 – 24448395.0 (mean 24448236.0) |
| `datacollectionperiod` | int64 | 0.0% | 158983.0 – 158992.0 (mean 158988.215) |
| `datacollection` | int64 | 0.0% | 168522.0 – 168525.0 (mean 168523.7383) |
| `scenario` | object | 0.0% | |
| `geographic_unit` | int64 | 0.0% | 26202.0 – 26246.0 (mean 26228.0561) |
| `datasourceorganization` | int64 | 0.0% | 2039.0 – 2039.0 (mean 2039.0) |
| `datasourcedocument` | int64 | 0.0% | 6617.0 – 6617.0 (mean 6617.0) |
| `dataseries` | int64 | 0.0% | 6502640.0 – 6502871.0 (mean 6502765.7103) |
| `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 | 2.0 | 1.6449 | 2.0 |
| `id` | 24448077.0 | 24448395.0 | 24448236.0 | 24448236.0 |
| `datacollectionperiod` | 158983.0 | 158992.0 | 158988.215 | 158989.0 |
| `datacollection` | 168522.0 | 168525.0 | 168523.7383 | 168524.0 |
| `geographic_unit` | 26202.0 | 26246.0 | 26228.0561 | 26229.0 |
| `datasourceorganization` | 2039.0 | 2039.0 | 2039.0 | 2039.0 |
| `datasourcedocument` | 6617.0 | 6617.0 | 6617.0 | 6617.0 |
| `dataseries` | 6502640.0 | 6502871.0 | 6502765.7103 | 6502769.0 |

---

## 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/sierra_leone_current_situation_fewsnet_ipc_classification) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_sierra_leone_current_situation_fewsnet_ipc_classification,
title = {Sierra Leone Current Situation FEWS NET Acute Food Insecurity Classifications Data},
author = {FEWS NET},
year = {2026},
url = {https://data.humdata.org/dataset/sierra_leone_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:
- 无注释
language_creators:
- 现有资源采集
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
- 表格回归
task_ids: []
tags:
- 非洲
- 人道主义
- 人道主义数据交换（HDX）
- Electric Sheep Africa
- 粮食安全
- 塞拉利昂（SLE）
pretty_name: "塞拉利昂当前局势FEWS NET急性粮食不安全分类数据"
dataset_info:
splits:
- name: train
num_examples: 85
- name: test
num_examples: 21
---

# 塞拉利昂当前局势FEWS NET急性粮食不安全分类数据

**发布方：** FEWS NET · **来源：** [人道主义数据交换（HDX）](https://data.humdata.org/dataset/sierra_leone_current_situation_fewsnet_ipc_classification) · **许可协议：** `CC BY` · **更新时间：** 2026-04-07

---

## 摘要

2015年塞拉利昂当前局势FEWS NET急性粮食不安全分类数据。
本数据集的每一行均代表一级行政单元的观测数据。时间覆盖范围由`projection_start`、`projection_end`两列标注。地理覆盖范围：**塞拉利昂（SLE）**。

*本数据集经[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为适配机器学习的Parquet格式（Parquet）。*

---

## 数据集特征

| | |
|---|---|
| **领域** | 粮食安全与营养 |
| **观测单元** | 一级行政单元 |
| **总数据行数** | 107 |
| **列数** | 40（其中9列为数值型、23列为分类型、7列为日期时间型） |
| **训练集拆分** | 85行 |
| **测试集拆分** | 21行 |
| **地理覆盖范围** | 塞拉利昂（SLE） |
| **发布方** | FEWS NET |
| **HDX最后更新时间** | 2026-04-07 |

---

## 变量

**地理类变量**：`country`（塞拉利昂）、`country_code`（SL）、`fewsnet_region`（西非）、`unit_type`（fsc_admin_lhz、fsc_admin）、`specialization_type`及另外2个变量。

**时间类变量**：`datacollectionperiod`（取值范围158983.0~158992.0）、`reporting_date`。

**结果/测量类变量**：`value`（取值范围1.0~2.0）。

**标识符/元数据类变量**：`source_organization`（FEWS NET、塞拉利昂）、`source_document`（《塞拉利昂粮食安全展望》）、`geographic_unit_full_name`（示例值：邦巴利粮食作物、辣椒、烟草与畜牧业，邦巴利，北部，塞拉利昂；科纳杜古畜牧业、粮食作物与贸易，科诺，东部，塞拉利昂；科诺-凯内马-博城水稻、林木与木材，科诺，东部，塞拉利昂）、`geographic_unit_name`（示例值：西部水稻、块根作物、谷物与贸易带，稻米主产区，沿海粮食作物与渔业区）、`fnid`（示例值：SL2016C3020102、SL2016C3010307、SL2016C3010306）及另外8个变量。

**其他类变量**：`geographic_group`（西非）、`classification_scale`、`is_allowing_for_assistance`、`projection_start`、`projection_end`及另外12个变量。

---

## 快速入门

python
from datasets import load_dataset

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

print(train.shape)
train.head()

---

## 数据模式

| 列名 | 数据类型 | 空值占比 | 取值范围/示例值 |
|---|---|---|---|
| `source_organization` | 对象型（object） | 0.0% | FEWS NET, 塞拉利昂 |
| `source_document` | 对象型（object） | 0.0% | 塞拉利昂粮食安全展望 |
| `country` | 对象型（object） | 0.0% | 塞拉利昂 |
| `country_code` | 对象型（object） | 0.0% | SL |
| `geographic_group` | 对象型（object） | 0.0% | 西非 |
| `fewsnet_region` | 对象型（object） | 0.0% | 西非 |
| `geographic_unit_full_name` | 对象型（object） | 0.0% | 邦巴利粮食作物、辣椒、烟草与畜牧业，邦巴利，北部，塞拉利昂；科纳杜古畜牧业、粮食作物与贸易，科诺，东部，塞拉利昂；科诺-凯内马-博城水稻、林木与木材，科诺，东部，塞拉利昂 |
| `geographic_unit_name` | 对象型（object） | 0.0% | 西部水稻、块根作物、谷物与贸易带，稻米主产区，沿海粮食作物与渔业区 |
| `unit_type` | 对象型（object） | 0.0% | fsc_admin_lhz, fsc_admin |
| `fnid` | 对象型（object） | 0.0% | SL2016C3020102, SL2016C3010307, SL2016C3010306 |
| `classification_scale` | 对象型（object） | 0.0% | 无 |
| `scenario_name` | 对象型（object） | 0.0% | 无 |
| `preference_rating` | 64位整型（int64） | 0.0% | 90.0 – 90.0（均值90.0） |
| `is_allowing_for_assistance` | 布尔型（bool） | 0.0% | 无 |
| `projection_start` | 日期时间型（datetime64[ns]） | 0.0% | 无 |
| `projection_end` | 日期时间型（datetime64[ns]） | 0.0% | 无 |
| `status` | 对象型（object） | 0.0% | 无 |
| `value` | 64位浮点型（float64） | 0.0% | 1.0 – 2.0（均值1.6449） |
| `description` | 对象型（object） | 0.0% | 无 |
| `id` | 64位整型（int64） | 0.0% | 24448077.0 – 24448395.0（均值24448236.0） |
| `datacollectionperiod` | 64位整型（int64） | 0.0% | 158983.0 – 158992.0（均值158988.215） |
| `datacollection` | 64位整型（int64） | 0.0% | 168522.0 – 168525.0（均值168523.7383） |
| `scenario` | 对象型（object） | 0.0% | 无 |
| `geographic_unit` | 64位整型（int64） | 0.0% | 26202.0 – 26246.0（均值26228.0561） |
| `datasourceorganization` | 64位整型（int64） | 0.0% | 2039.0 – 2039.0（均值2039.0） |
| `datasourcedocument` | 64位整型（int64） | 0.0% | 6617.0 – 6617.0（均值6617.0） |
| `dataseries` | 64位整型（int64） | 0.0% | 6502640.0 – 6502871.0（均值6502765.7103） |
| `dataseries_name` | 对象型（object） | 0.0% | 无 |
| `specialization_type` | 对象型（object） | 0.0% | 无 |
| `dataseries_specialization_type` | 对象型（object） | 0.0% | 无 |
| `data_usage_policy` | 对象型（object） | 0.0% | 无 |
| `created` | 日期时间型（datetime64[ns]） | 0.0% | 无 |
| `modified` | 日期时间型（datetime64[ns]） | 0.0% | 无 |
| `status_changed` | 日期时间型（datetime64[ns]） | 0.0% | 无 |
| `collection_status` | 对象型（object） | 0.0% | 无 |
| `collection_status_changed` | 日期时间型（datetime64[ns]） | 0.0% | 无 |
| `collection_schedule` | 对象型（object） | 0.0% | 无 |
| `reporting_date` | 日期时间型（datetime64[ns]） | 0.0% | 无 |
| `esa_source` | 对象型（object） | 0.0% | 无 |
| `esa_processed` | 对象型（object） | 0.0% | 无 |

---

## 数值摘要

| 列名 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|---|
| `preference_rating` | 90.0 | 90.0 | 90.0 | 90.0 |
| `value` | 1.0 | 2.0 | 1.6449 | 2.0 |
| `id` | 24448077.0 | 24448395.0 | 24448236.0 | 24448236.0 |
| `datacollectionperiod` | 158983.0 | 158992.0 | 158988.215 | 158989.0 |
| `datacollection` | 168522.0 | 168525.0 | 168523.7383 | 168524.0 |
| `geographic_unit` | 26202.0 | 26246.0 | 26228.0561 | 26229.0 |
| `datasourceorganization` | 2039.0 | 2039.0 | 2039.0 | 2039.0 |
| `datasourcedocument` | 6617.0 | 6617.0 | 6617.0 | 6617.0 |
| `dataseries` | 6502640.0 | 6502871.0 | 6502765.7103 | 6502769.0 |

---

## 数据整理

原始数据通过CKAN应用程序编程接口（CKAN API）从人道主义数据交换（HDX）下载，并转换为Parquet格式（Parquet）。列名统一转换为小写，并采用蛇形命名法（snake_case）进行标准化。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3个缺失值占比超过80%的列：`pct_phase3`、`pct_phase4`、`pct_phase5`。根据解析成功率（阈值为85%），将7个列从字符串类型转换为数值型或日期时间型。本数据集采用固定随机种子（42）以80/20的比例划分为训练集与测试集，并以Snappy压缩的Parquet格式保存。

---

## 局限性

- 数据源自FEWS NET，未经过Electric Sheep Africa的独立验证。
- 自动化数据清洗无法修正原始数据收集中的错报值、定义不一致或抽样偏差问题。
- 请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/sierra_leone_current_situation_fewsnet_ipc_classification)以获取发布方提供的方法说明与注意事项。

---

## 引用

bibtex
@dataset{hdx_africa_sierra_leone_current_situation_fewsnet_ipc_classification,
title = {Sierra Leone Current Situation FEWS NET Acute Food Insecurity Classifications Data},
author = {FEWS NET},
year = {2026},
url = {https://data.humdata.org/dataset/sierra_leone_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) — 非洲机器学习数据集基础设施，尼日利亚拉各斯。*