electricsheepafrica/africa-3w-operational-presence-may-2016

---
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:
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- education
- who-is-doing-what-and-where-3w-4w-5w
- eth
pretty_name: "Ethiopia - 3W Operational Presence May 2016"
dataset_info:
splits:
- name: train
num_examples: 1640
- name: test
num_examples: 410
---

# Ethiopia - 3W Operational Presence May 2016

**Publisher:** OCHA Ethiopia · **Source:** [HDX](https://data.humdata.org/dataset/3w-operational-presence-may-2016) · **License:** `cc-by` · **Updated:** 2023-03-03

---

## Abstract

The Who Does What Where is a core humanitarian dataset for coordination. This data contains operational presence of humanitarian partners in Ethiopia at admin3 level by cluster in CSV format.

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2023-03-03. Geographic scope: **ETH**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Education |
| **Unit of observation** | Subnational administrative unit observations |
| **Rows (total)** | 2,050 |
| **Columns** | 14 (1 numeric, 13 categorical, 0 datetime) |
| **Train split** | 1,640 rows |
| **Test split** | 410 rows |
| **Geographic scope** | ETH |
| **Publisher** | OCHA Ethiopia |
| **HDX last updated** | 2023-03-03 |

---

## Variables

**Geographic** — `organization_type` (International NGO, Government, United Nations), `region` (Amhara, Oromia, SNNPR), `zone` (North Wollo, Wag Himra, East Harerge), `woreda` (Sekota, Guba Lafto, Kobo), `woreda_code` (range 10101.0–508061.0) and 1 others.

**Identifier / Metadata** — `esa_source`, `esa_processed`.

**Other** — `organization` (NDRMC, SCI, CRS), `sector` (WASH, Food, Agriculture), `subsector` (General food distribution, Crop, Water), `activities` (Food Distributions, CMAM, Construction of motorized borehole), `project_status` (Ongoing) and 1 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-3w-operational-presence-may-2016")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `organization` | object | 0.0% | NDRMC, SCI, CRS |
| `organization_type` | object | 0.0% | International NGO, Government, United Nations |
| `region` | object | 0.0% | Amhara, Oromia, SNNPR |
| `zone` | object | 0.0% | North Wollo, Wag Himra, East Harerge |
| `woreda` | object | 0.0% | Sekota, Guba Lafto, Kobo |
| `woreda_code` | float64 | 0.0% | 10101.0 – 508061.0 (mean 41637.286) |
| `project_type` | object | 0.0% | Emergency Response |
| `sector` | object | 0.0% | WASH, Food, Agriculture |
| `subsector` | object | 13.8% | General food distribution, Crop, Water |
| `activities` | object | 6.7% | Food Distributions, CMAM, Construction of motorized borehole |
| `project_status` | object | 0.0% | Ongoing |
| `implementing_partner_s` | object | 0.0% | |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `woreda_code` | 10101.0 | 508061.0 | 41637.286 | 40503.0 |

---

## 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`. 1,887 exact duplicate rows were removed. 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/3w-operational-presence-may-2016) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_3w_operational_presence_may_2016,
title = {Ethiopia - 3W Operational Presence May 2016},
author = {OCHA Ethiopia},
year = {2023},
url = {https://data.humdata.org/dataset/3w-operational-presence-may-2016},
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.*