electricsheepafrica/africa-2019-hno-people-in-need-by-admin-level3

---
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-regression
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- people-in-need-pin
- eth
pretty_name: "Ethiopia HNO PiN 2019"
dataset_info:
splits:
- name: train
num_examples: 746
- name: test
num_examples: 186
---

# Ethiopia HNO PiN 2019

**Publisher:** OCHA Ethiopia · **Source:** [HDX](https://data.humdata.org/dataset/2019-hno-people-in-need-by-admin-level3) · **License:** `cc-by` · **Updated:** 2025-05-05

---

## Abstract

The file contains the proposed estimated People In Need (PIN) number by admin level 3 for the 2019 Humanitarian Needs Overview (HNO)

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2025-05-05. Geographic scope: **ETH**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Demographics and population |
| **Unit of observation** | Subnational administrative unit observations |
| **Rows (total)** | 933 |
| **Columns** | 38 (32 numeric, 6 categorical, 0 datetime) |
| **Train split** | 746 rows |
| **Test split** | 186 rows |
| **Geographic scope** | ETH |
| **Publisher** | OCHA Ethiopia |
| **HDX last updated** | 2025-05-05 |

---

## Variables

**Geographic** — `region` (Oromia, Amhara, SNNP), `zone` (North Shewa, Arsi, South Wello), `woreda` (Tahtay Adiyabo, Bilcil-bur, Kebribeyah), `total_population` (range 596.2348–96527522.79), `non_displaced_acute` (range 0.0–3214567.446) and 9 others.

**Demographic** — `idp_male_children` (range 0.0–388668.0852), `idp_female_children` (range 0.0–407130.8182), `idp_male_adult` (range 0.0–233337.1637), `idp_female_adult` (range 0.0–238769.6007), `idp_male_children2` (range 0.0–532474.7005) and 11 others.

**Outcome / Measurement** — `total_acute` (range 0.0–4569882.446), `idps_moderate` (range 0.0–1833362.0), `total_moderate` (range 5.9623–4294374.272), `grand_total` (range 5.9623–8864256.718).

**Identifier / Metadata** — `pcode` (ET010101, ET050392, ET050206), `idps_acute` (range 0.0–1355315.0), `esa_source` (HDX), `esa_processed` (2026-04-10).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-2019-hno-people-in-need-by-admin-level3")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `region` | object | 0.0% | Oromia, Amhara, SNNP |
| `zone` | object | 0.1% | North Shewa, Arsi, South Wello |
| `woreda` | object | 0.1% | Tahtay Adiyabo, Bilcil-bur, Kebribeyah |
| `pcode` | object | 0.1% | ET010101, ET050392, ET050206 |
| `total_population` | float64 | 0.0% | 596.2348 – 96527522.79 (mean 206918.5912) |
| `idps_acute` | int64 | 0.0% | 0.0 – 1355315.0 (mean 2905.284) |
| `non_displaced_acute` | float64 | 0.0% | 0.0 – 3214567.446 (mean 6890.8198) |
| `total_acute` | float64 | 0.0% | 0.0 – 4569882.446 (mean 9796.1038) |
| `idps_moderate` | int64 | 0.0% | 0.0 – 1833362.0 (mean 3930.0364) |
| `non_displaced_moderate` | float64 | 0.0% | 0.0 – 2461012.272 (mean 5275.4818) |
| `total_moderate` | float64 | 0.0% | 5.9623 – 4294374.272 (mean 9205.5183) |
| `grand_total` | float64 | 0.0% | 5.9623 – 8864256.718 (mean 19001.6221) |
| `idp_male_children` | float64 | 0.0% | 0.0 – 388668.0852 (mean 833.1577) |
| `idp_female_children` | float64 | 0.0% | 0.0 – 407130.8182 (mean 872.7349) |
| `idp_male_adult` | float64 | 0.0% | 0.0 – 233337.1637 (mean 500.1868) |
| `idp_female_adult` | float64 | 0.0% | 0.0 – 238769.6007 (mean 511.8319) |
| `idp_male_elderly` | float64 | 0.0% | 0.0 – 44187.5128 (mean 94.7214) |
| `idp_female_elderly` | float64 | 0.0% | 0.0 – 43221.8194 (mean 92.6513) |
| `idp_male_children2` | float64 | 0.0% | 0.0 – 532474.7005 (mean 1141.4249) |
| `idp_female_children2` | float64 | 0.0% | 0.0 – 561056.4169 (mean 1202.6933) |
| `idp_male_adult2` | float64 | 0.0% | 0.0 – 294582.2988 (mean 631.4733) |
| `idp_female_adult2` | float64 | 0.0% | 0.0 – 313881.6121 (mean 672.8438) |
| `idp_male_elderly2` | float64 | 0.0% | 0.0 – 64992.5174 (mean 139.3194) |
| `idp_female_elderly2` | float64 | 0.0% | 0.0 – 66374.4543 (mean 142.2818) |
| `nd_male_children` | float64 | 0.0% | |
| `nd_female_children` | float64 | 0.0% | |
| `nd_male_adult` | float64 | 0.0% | |
| `nd_female_adult` | float64 | 0.0% | |
| `nd_male_elderly` | float64 | 0.0% | |
| `nd_female_elderly` | float64 | 0.0% | |
| `nd_male_children2` | float64 | 0.0% | |
| `nd_female_children3` | float64 | 0.0% | |
| `nd_male_adult4` | float64 | 0.0% | |
| `nd_female_adult5` | float64 | 0.0% | |
| `nd_male_elderly6` | float64 | 0.0% | |
| `nd_female_elderly7` | float64 | 0.0% | |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-10 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `total_population` | 596.2348 | 96527522.79 | 206918.5912 | 92197.0785 |
| `idps_acute` | 0.0 | 1355315.0 | 2905.284 | 0.0 |
| `non_displaced_acute` | 0.0 | 3214567.446 | 6890.8198 | 0.0 |
| `total_acute` | 0.0 | 4569882.446 | 9796.1038 | 233.0 |
| `idps_moderate` | 0.0 | 1833362.0 | 3930.0364 | 0.0 |
| `non_displaced_moderate` | 0.0 | 2461012.272 | 5275.4818 | 931.048 |
| `total_moderate` | 5.9623 | 4294374.272 | 9205.5183 | 1199.5484 |
| `grand_total` | 5.9623 | 8864256.718 | 19001.6221 | 2131.8869 |
| `idp_male_children` | 0.0 | 388668.0852 | 833.1577 | 0.0 |
| `idp_female_children` | 0.0 | 407130.8182 | 872.7349 | 0.0 |
| `idp_male_adult` | 0.0 | 233337.1637 | 500.1868 | 0.0 |
| `idp_female_adult` | 0.0 | 238769.6007 | 511.8319 | 0.0 |
| `idp_male_elderly` | 0.0 | 44187.5128 | 94.7214 | 0.0 |
| `idp_female_elderly` | 0.0 | 43221.8194 | 92.6513 | 0.0 |
| `idp_male_children2` | 0.0 | 532474.7005 | 1141.4249 | 0.0 |

---

## 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`. 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 OCHA Ethiopia 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/2019-hno-people-in-need-by-admin-level3) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_2019_hno_people_in_need_by_admin_level3,
title = {Ethiopia HNO PiN 2019},
author = {OCHA Ethiopia},
year = {2025},
url = {https://data.humdata.org/dataset/2019-hno-people-in-need-by-admin-level3},
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:
- 表格回归
- 其他
task_ids:
- []
tags:
- 非洲
- 人道主义
- HDX
- electric-sheep-africa
- 受援需求人群（PiN）
- ETH
pretty_name: "埃塞俄比亚2019年人道主义需求概览受援需求人群数据集"
dataset_info:
splits:
- name: train
num_examples: 746
- name: test
num_examples: 186

# 埃塞俄比亚2019年人道主义需求概览受援需求人群数据集

**发布方**：埃塞俄比亚人道主义事务协调厅（OCHA Ethiopia） · **来源**：[HDX](https://data.humdata.org/dataset/2019-hno-people-in-need-by-admin-level3) · **授权协议**：`cc-by` · **更新时间**：2025-05-05

---

## 摘要
本数据集包含2019年人道主义需求概览（Humanitarian Needs Overview，简称HNO）中按三级行政单元划分的预估受援需求人群（People In Need，简称PiN）数量。
数据集中每一行代表一个次国家级行政单元的观测样本。本数据最后于2025-05-05在HDX平台更新。地理覆盖范围：**ETH（埃塞俄比亚）**。
*本数据集已由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为适用于机器学习的Parquet格式。*

---

## 数据集特征

| 类别 | 详情 |
|---|---|
| **研究领域** | 人口与人口统计学 |
| **观测单元** | 次国家级行政单元观测样本 |
| **总样本行数** | 933 |
| **字段数** | 38（32个数值型字段、6个分类型字段、0个日期时间型字段） |
| **训练集划分** | 746条样本 |
| **测试集划分** | 186条样本 |
| **地理覆盖范围** | 埃塞俄比亚（ETH） |
| **发布方** | 埃塞俄比亚人道主义事务协调厅 |
| **HDX平台最后更新时间** | 2025-05-05 |

---

## 字段分类

**地理类字段**：`region`（奥罗米亚州、阿姆哈拉州、南民族州）、`zone`（北舍瓦区、阿尔西区、南韦洛区）、`woreda`（塔哈泰·阿迪亚博、比尔西尔布尔、凯布里贝亚）、`total_population`（取值范围596.2348–96527522.79）、`non_displaced_acute`（取值范围0.0–3214567.446）等共9个字段。

**人口统计类字段**：`idp_male_children`（取值范围0.0–388668.0852）、`idp_female_children`（取值范围0.0–407130.8182）、`idp_male_adult`（取值范围0.0–233337.1637）、`idp_female_adult`（取值范围0.0–238769.6007）、`idp_male_children2`（取值范围0.0–532474.7005）等共11个字段。

**结果/测量类字段**：`total_acute`（取值范围0.0–4569882.446）、`idps_moderate`（取值范围0.0–1833362.0）、`total_moderate`（取值范围5.9623–4294374.272）、`grand_total`（取值范围5.9623–8864256.718）。

**标识符/元数据字段**：`pcode`（ET010101、ET050392、ET050206）、`idps_acute`（取值范围0.0–1355315.0）、`esa_source`（HDX）、`esa_processed`（2026-04-10）。

---

## 快速上手

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-2019-hno-people-in-need-by-admin-level3")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()

---

## 数据 schema

| 字段名 | 数据类型 | 空值占比 | 取值范围/示例值 |
|---|---|---|---|
| `region` | object型 | 0.0% | 奥罗米亚州、阿姆哈拉州、南民族州 |
| `zone` | object型 | 0.1% | 北舍瓦区、阿尔西区、南韦洛区 |
| `woreda` | object型 | 0.1% | 塔哈泰·阿迪亚博、比尔西尔布尔、凯布里贝亚 |
| `pcode` | object型 | 0.1% | ET010101、ET050392、ET050206 |
| `total_population` | float64型 | 0.0% | 596.2348 – 96527522.79（均值206918.5912） |
| `idps_acute` | int64型 | 0.0% | 0.0 – 1355315.0（均值2905.284） |
| `non_displaced_acute` | float64型 | 0.0% | 0.0 – 3214567.446（均值6890.8198） |
| `total_acute` | float64型 | 0.0% | 0.0 – 4569882.446（均值9796.1038） |
| `idps_moderate` | int64型 | 0.0% | 0.0 – 1833362.0（均值3930.0364） |
| `non_displaced_moderate` | float64型 | 0.0% | 0.0 – 2461012.272（均值5275.4818） |
| `total_moderate` | float64型 | 0.0% | 5.9623 – 4294374.272（均值9205.5183） |
| `grand_total` | float64型 | 0.0% | 5.9623 – 8864256.718（均值19001.6221） |
| `idp_male_children` | float64型 | 0.0% | 0.0 – 388668.0852（均值833.1577） |
| `idp_female_children` | float64型 | 0.0% | 0.0 – 407130.8182（均值872.7349） |
| `idp_male_adult` | float64型 | 0.0% | 0.0 – 233337.1637（均值500.1868） |
| `idp_female_adult` | float64型 | 0.0% | 0.0 – 238769.6007（均值511.8319） |
| `idp_male_elderly` | float64型 | 0.0% | 0.0 – 44187.5128（均值94.7214） |
| `idp_female_elderly` | float64型 | 0.0% | 0.0 – 43221.8194（均值92.6513） |
| `idp_male_children2` | float64型 | 0.0% | 0.0 – 532474.7005（均值1141.4249） |
| `idp_female_children2` | float64型 | 0.0% | 0.0 – 561056.4169（均值1202.6933） |
| `idp_male_adult2` | float64型 | 0.0% | 0.0 – 294582.2988（均值631.4733） |
| `idp_female_adult2` | float64型 | 0.0% | 0.0 – 313881.6121（均值672.8438） |
| `idp_male_elderly2` | float64型 | 0.0% | 0.0 – 64992.5174（均值139.3194） |
| `idp_female_elderly2` | float64型 | 0.0% | 0.0 – 66374.4543（均值142.2818） |
| `nd_male_children` | float64型 | 0.0% | |
| `nd_female_children` | float64型 | 0.0% | |
| `nd_male_adult` | float64型 | 0.0% | |
| `nd_female_adult` | float64型 | 0.0% | |
| `nd_male_elderly` | float64型 | 0.0% | |
| `nd_female_elderly` | float64型 | 0.0% | |
| `nd_male_children2` | float64型 | 0.0% | |
| `nd_female_children3` | float64型 | 0.0% | |
| `nd_male_adult4` | float64型 | 0.0% | |
| `nd_female_adult5` | float64型 | 0.0% | |
| `nd_male_elderly6` | float64型 | 0.0% | |
| `nd_female_elderly7` | float64型 | 0.0% | |
| `esa_source` | object型 | 0.0% | HDX |
| `esa_processed` | object型 | 0.0% | 2026-04-10 |

---

## 数值型字段统计摘要

| 字段名 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|---|
| `total_population` | 596.2348 | 96527522.79 | 206918.5912 | 92197.0785 |
| `idps_acute` | 0.0 | 1355315.0 | 2905.284 | 0.0 |
| `non_displaced_acute` | 0.0 | 3214567.446 | 6890.8198 | 0.0 |
| `total_acute` | 0.0 | 4569882.446 | 9796.1038 | 233.0 |
| `idps_moderate` | 0.0 | 1833362.0 | 3930.0364 | 0.0 |
| `non_displaced_moderate` | 0.0 | 2461012.272 | 5275.4818 | 931.048 |
| `total_moderate` | 5.9623 | 4294374.272 | 9205.5183 | 1199.5484 |
| `grand_total` | 5.9623 | 8864256.718 | 19001.6221 | 2131.8869 |
| `idp_male_children` | 0.0 | 388668.0852 | 833.1577 | 0.0 |
| `idp_female_children` | 0.0 | 407130.8182 | 872.7349 | 0.0 |
| `idp_male_adult` | 0.0 | 233337.1637 | 500.1868 | 0.0 |
| `idp_female_adult` | 0.0 | 238769.6007 | 511.8319 | 0.0 |
| `idp_male_elderly` | 0.0 | 44187.5128 | 94.7214 | 0.0 |
| `idp_female_elderly` | 0.0 | 43221.8194 | 92.6513 | 0.0 |
| `idp_male_children2` | 0.0 | 532474.7005 | 1141.4249 | 0.0 |

---

## 数据整理流程
原始数据通过CKAN API从HDX平台下载，并转换为Parquet格式。所有字段名均转为小写并标准化为蛇形命名法（snake_case）。通用缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）被统一替换为`NaN`。本数据集以固定随机种子（42）按80/20的比例划分为训练集与测试集，并以Snappy压缩格式的Parquet文件保存。

---

## 数据集局限性
1. 本数据源自埃塞俄比亚人道主义事务协调厅，尚未由Electric Sheep Africa（ESA）进行独立验证。
2. 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。
3. 如需了解发布方的方法论说明与免责条款，请参阅[HDX平台原始数据集页面](https://data.humdata.org/dataset/2019-hno-people-in-need-by-admin-level3)。

---

## 引用格式

bibtex
@dataset{hdx_africa_2019_hno_people_in_need_by_admin_level3,
title = {Ethiopia HNO PiN 2019},
author = {OCHA Ethiopia},
year = {2025},
url = {https://data.humdata.org/dataset/2019-hno-people-in-need-by-admin-level3},
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。*