electricsheepafrica/africa-unesco-data-for-namibia

---
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:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- demographics
- education
- indicators
- socioeconomics
- sustainable-development
- sustainable-development-goals-sdg
- nam
pretty_name: "Namibia - Education Indicators"
dataset_info:
splits:
- name: train
num_examples: 4865
- name: test
num_examples: 1216
---

# Namibia - Education Indicators

**Publisher:** UNESCO · **Source:** [HDX](https://data.humdata.org/dataset/unesco-data-for-namibia) · **License:** `cc-by-igo` · **Updated:** 2026-03-02

---

## Abstract

Education indicators for Namibia.

Contains data from the UNESCO Institute for Statistics [bulk data service](http://data.uis.unesco.org) covering the following categories: SDG 4 Global and Thematic (made 2026 February), Other Policy Relevant Indicators (made 2026 February), Demographic and Socio-economic (made 2026 February)

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-02. Geographic scope: **NAM**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Education |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 6,082 |
| **Columns** | 6 (2 numeric, 4 categorical, 0 datetime) |
| **Train split** | 4,865 rows |
| **Test split** | 1,216 rows |
| **Geographic scope** | NAM |
| **Publisher** | UNESCO |
| **HDX last updated** | 2026-03-02 |

---

## Variables

**Geographic** — `country_id` (NAM), `year` (range 1981.0–2025.0).

**Outcome / Measurement** — `value` (range 0.0–1611893.0).

**Identifier / Metadata** — `indicator_id` (CR.MOD.1.F, CR.MOD.1.GPIA, CR.MOD.1.M), `esa_source` (HDX), `esa_processed` (2026-04-04).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-unesco-data-for-namibia")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `indicator_id` | object | 0.0% | CR.MOD.1.F, CR.MOD.1.GPIA, CR.MOD.1.M |
| `country_id` | object | 0.0% | NAM |
| `year` | int64 | 0.0% | 1981.0 – 2025.0 (mean 2010.9998) |
| `value` | float64 | 0.0% | 0.0 – 1611893.0 (mean 2772.7203) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-04 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1981.0 | 2025.0 | 2010.9998 | 2013.0 |
| `value` | 0.0 | 1611893.0 | 2772.7203 | 18.7802 |

---

## 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`. 2 column(s) with >80% missing values were removed: `magnitude`, `qualifier`. 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 UNESCO 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/unesco-data-for-namibia) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_unesco_data_for_namibia,
title = {Namibia - Education Indicators},
author = {UNESCO},
year = {2026},
url = {https://data.humdata.org/dataset/unesco-data-for-namibia},
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.*