electricsheepafrica/africa-world-bank-education-indicators-for-south-africa

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- education
- indicators
- zaf
pretty_name: "South Africa - Education"
dataset_info:
splits:
- name: train
num_examples: 11705
- name: test
num_examples: 2926
---

# South Africa - Education

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-education-indicators-for-south-africa) · **License:** `cc-by` · **Updated:** 2026-03-27

---

## Abstract

Contains data from the World Bank's [data portal](http://data.worldbank.org/). There is also a [consolidated country dataset](https://data.humdata.org/dataset/world-bank-combined-indicators-for-south-africa) on HDX.

Education is one of the most powerful instruments for reducing poverty and inequality and lays a foundation for sustained economic growth. The World Bank compiles data on education inputs, participation, efficiency, and outcomes. Data on education are compiled by the United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics from official responses to surveys and from reports provided by education authorities in each country.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: **ZAF**.

*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)** | 14,632 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 11,705 rows |
| **Test split** | 2,926 rows |
| **Geographic scope** | ZAF |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-03-27 |

---

## Variables

**Geographic** — `country_name` (South Africa), `country_iso3` (ZAF), `year` (range 1960.0–2025.0).

**Outcome / Measurement** — `value` (range 0.0–26731522.0).

**Identifier / Metadata** — `indicator_name` (Population ages 15-64 (% of total population), Population ages 0-14 (% of total population), Primary education, duration (years)), `indicator_code` (SP.POP.1564.TO.ZS, SP.POP.0014.TO.ZS, SE.PRM.DURS), `esa_source` (HDX), `esa_processed` (2026-04-10).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-education-indicators-for-south-africa")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `country_name` | object | 0.0% | South Africa |
| `country_iso3` | object | 0.0% | ZAF |
| `year` | int64 | 0.0% | 1960.0 – 2025.0 (mean 1999.2618) |
| `indicator_name` | object | 0.0% | Population ages 15-64 (% of total population), Population ages 0-14 (% of total population), Primary education, duration (years) |
| `indicator_code` | object | 0.0% | SP.POP.1564.TO.ZS, SP.POP.0014.TO.ZS, SE.PRM.DURS |
| `value` | float64 | 0.0% | 0.0 – 26731522.0 (mean 1043865.8018) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-10 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1960.0 | 2025.0 | 1999.2618 | 2001.0 |
| `value` | 0.0 | 26731522.0 | 1043865.8018 | 2875.0 |

---

## 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`. 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 World Bank Group 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/world-bank-education-indicators-for-south-africa) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_world_bank_education_indicators_for_south_africa,
title = {South Africa - Education},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-education-indicators-for-south-africa},
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.*