electricsheepafrica/africa-nigeria-northeast-nigeria-3ws

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
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
- hxl
- nga
pretty_name: "Nigeria: Northeast Nigeria 3Ws"
dataset_info:
splits:
- name: train
num_examples: 3132
- name: test
num_examples: 783
---

# Nigeria: Northeast Nigeria 3Ws

**Publisher:** OCHA Nigeria · **Source:** [HDX](https://data.humdata.org/dataset/nigeria-northeast-nigeria-3ws) · **License:** `other-pd-nr` · **Updated:** 2025-04-10

---

## Abstract

who-is-doing-what-and-where

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2025-04-10. Geographic scope: **NGA**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | Subnational administrative unit observations |
| **Rows (total)** | 3,915 |
| **Columns** | 18 (1 numeric, 17 categorical, 0 datetime) |
| **Train split** | 3,132 rows |
| **Test split** | 783 rows |
| **Geographic scope** | NGA |
| **Publisher** | OCHA Nigeria |
| **HDX last updated** | 2025-04-10 |

---

## Variables

**Geographic** — `org_acronym` (IOM, UNICEF, DRC), `type_of_organization` (International NGO, UN Agency, National NGO), `operation_type` (Reporting, Implementing, #operation+type), `states` (Borno, Adamawa, Yobe), `state_pcode` (NGA008, NGA002, NGA036) and 4 others.

**Temporal** — `month`.

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

**Other** — `organization` (International Organization for Migration, United Nations Children's Emergency Fund, Danish Refugee Council), `project_sector` (Protection, Water, Sanitation & Hygiene, Nutrition), `activities` (Health Activities, Agriculture & Livelihoods, Sensitization and community engagement), `status` (Ongoing, Completed, Planned), `ishrp` and 1 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-nigeria-northeast-nigeria-3ws")
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% | International Organization for Migration, United Nations Children's Emergency Fund, Danish Refugee Council |
| `org_acronym` | object | 0.0% | IOM, UNICEF, DRC |
| `type_of_organization` | object | 0.0% | International NGO, UN Agency, National NGO |
| `operation_type` | object | 0.0% | Reporting, Implementing, #operation+type |
| `project_sector` | object | 0.0% | Protection, Water, Sanitation & Hygiene, Nutrition |
| `activities` | object | 0.0% | Health Activities, Agriculture & Livelihoods, Sensitization and community engagement |
| `status` | object | 0.0% | Ongoing, Completed, Planned |
| `states` | object | 0.0% | Borno, Adamawa, Yobe |
| `state_pcode` | object | 0.0% | NGA008, NGA002, NGA036 |
| `lga` | object | 0.0% | Maiduguri, Jere, Gwoza |
| `lga_pcode` | object | 0.0% | |
| `ishrp` | object | 0.0% | |
| `response_type` | object | 0.0% | |
| `isrp` | object | 0.0% | |
| `month` | object | 0.0% | |
| `year` | float64 | 0.0% | 2021.0 – 2021.0 (mean 2021.0) |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 2021.0 | 2021.0 | 2021.0 | 2021.0 |

---

## 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 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 Nigeria 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/nigeria-northeast-nigeria-3ws) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_nigeria_northeast_nigeria_3ws,
title = {Nigeria: Northeast Nigeria 3Ws},
author = {OCHA Nigeria},
year = {2025},
url = {https://data.humdata.org/dataset/nigeria-northeast-nigeria-3ws},
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.*