electricsheepafrica/africa-ethiopia-pin-targeted-reached-by-location-and-cluster

---
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
- affected-population
- drought
- hxl
- people-in-need-pin
- eth
pretty_name: "Ethiopia Drought Related - People Affected, Targeted & Reached by Location"
dataset_info:
splits:
- name: train
num_examples: 312
- name: test
num_examples: 78
---

# Ethiopia Drought Related - People Affected, Targeted & Reached by Location

**Publisher:** OCHA Regional Office for Southern and Eastern Africa (ROSEA) · **Source:** [HDX](https://data.humdata.org/dataset/ethiopia-pin-targeted-reached-by-location-and-cluster) · **License:** `cc-by` · **Updated:** 2025-09-16

---

## Abstract

Drought affected areas and population in Ethiopia

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-09-16. Geographic scope: **ETH**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Natural hazards and disaster risk |
| **Unit of observation** | Tabular records |
| **Rows (total)** | 391 |
| **Columns** | 12 (4 numeric, 8 categorical, 0 datetime) |
| **Train split** | 312 rows |
| **Test split** | 78 rows |
| **Geographic scope** | ETH |
| **Publisher** | OCHA Regional Office for Southern and Eastern Africa (ROSEA) |
| **HDX last updated** | 2025-09-16 |

---

## Variables

**Geographic** — `location` (admin3Pcode, ET050586, ET050788), `operational_priority` (range 1.0–3.0).

**Identifier / Metadata** — `unnamed_1` (Woreda, Marsin, Daratole), `unnamed_2` (East Hararge, West Hararge, Guji), `unnamed_3` (ET0410, ET0409, ET0414), `unnamed_4` (Oromia, Somali, SNNP), `unnamed_5` (ET04, ET05, ET07) and 4 others.

**Other** — `overall_figures` (range 23.0–283966.0).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-ethiopia-pin-targeted-reached-by-location-and-cluster")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `location` | object | 0.0% | admin3Pcode, ET050586, ET050788 |
| `unnamed_1` | object | 0.0% | Woreda, Marsin, Daratole |
| `unnamed_2` | object | 0.0% | East Hararge, West Hararge, Guji |
| `unnamed_3` | object | 0.0% | ET0410, ET0409, ET0414 |
| `unnamed_4` | object | 0.0% | Oromia, Somali, SNNP |
| `unnamed_5` | object | 0.0% | ET04, ET05, ET07 |
| `operational_priority` | float64 | 0.5% | 1.0 – 3.0 (mean 2.2391) |
| `overall_figures` | float64 | 0.5% | 23.0 – 283966.0 (mean 41606.928) |
| `unnamed_8` | float64 | 0.5% | 104.0 – 342553.0 (mean 33482.9846) |
| `unnamed_9` | float64 | 0.5% | 0.0 – 255985.0 (mean 20282.5501) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-10 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `operational_priority` | 1.0 | 3.0 | 2.2391 | 2.0 |
| `overall_figures` | 23.0 | 283966.0 | 41606.928 | 30987.0 |
| `unnamed_8` | 104.0 | 342553.0 | 33482.9846 | 21742.0 |
| `unnamed_9` | 0.0 | 255985.0 | 20282.5501 | 5508.0 |

---

## 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`. 4 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 OCHA Regional Office for Southern and Eastern Africa (ROSEA) 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/ethiopia-pin-targeted-reached-by-location-and-cluster) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_ethiopia_pin_targeted_reached_by_location_and_cluster,
title = {Ethiopia Drought Related - People Affected, Targeted & Reached by Location},
author = {OCHA Regional Office for Southern and Eastern Africa (ROSEA)},
year = {2025},
url = {https://data.humdata.org/dataset/ethiopia-pin-targeted-reached-by-location-and-cluster},
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.*