electricsheepafrica/africa-children-below-18-and-married

---
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:
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- below-18
- children
- marriage
- uganda
pretty_name: "Children below 18 and married | Uganda"
dataset_info:
splits:
- name: train
num_examples: 89
- name: test
num_examples: 22
---

# Children below 18 and married | Uganda

**Publisher:** Code for Africa · **Source:** [OpenAfrica](https://open.africa/dataset/children-below-18-and-married) · **License:** `cc-by` · **Updated:** 2023-11-30

---

## Abstract

This dataset contains humanitarian and development data records covering Africa (multiple countries), comprising 112 observations across 7 variables.

Each row in this dataset represents subnational administrative unit observations. Data was last updated on OpenAfrica on 2023-11-30. Geographic scope: **Africa (multiple countries)**.

*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)** | 112 |
| **Columns** | 7 (3 numeric, 4 categorical, 0 datetime) |
| **Train split** | 89 rows |
| **Test split** | 22 rows |
| **Geographic scope** | Africa (multiple countries) |
| **Publisher** | Code for Africa |
| **OpenAfrica last updated** | 2023-11-30 |

---

## Variables

**Geographic** — `district` (Buikwe , Bukomansimbi , Oyam ).

**Demographic** — `male_married` (range 0.5–3.3), `female_married` (range 3.0–11.6).

**Identifier / Metadata** — `code` (range 1.0–456.0), `esa_source` (HDX), `esa_processed` (2026-04-27).

**Other** — `level` (district).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-children-below-18-and-married")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `code` | int64 | 0.0% | 1.0 – 456.0 (mean 92.7321) |
| `level` | object | 0.0% | district |
| `district` | object | 0.0% | Buikwe , Bukomansimbi , Oyam |
| `male_married` | float64 | 0.0% | 0.5 – 3.3 (mean 1.4321) |
| `female_married` | float64 | 0.0% | 3.0 – 11.6 (mean 5.8241) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-27 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `code` | 1.0 | 456.0 | 92.7321 | 62.5 |
| `male_married` | 0.5 | 3.3 | 1.4321 | 1.4 |
| `female_married` | 3.0 | 11.6 | 5.8241 | 5.7 |

---

## Curation

Raw data was downloaded from OpenAfrica 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 Code for Africa 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://open.africa/dataset/children-below-18-and-married) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{openafrica_africa_children_below_18_and_married,
title = {Children below 18 and married | Uganda},
author = {Code for Africa},
year = {2023},
url = {https://open.africa/dataset/children-below-18-and-married},
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.*