---
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:
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- education
- indicators
- literacy
- eri
pretty_name: "ERITREA - Education indicators, UNECA"
dataset_info:
  splits:
    - name: train
      num_examples: 39
    - name: test
      num_examples: 9
---

# ERITREA - Education indicators, UNECA

**Publisher:** United Nations Economic Commission for Africa · **Source:** [HDX](https://data.humdata.org/dataset/eritrea-uneca-education) · **License:** `cc-by-igo` · **Updated:** 2024-09-13

---

## Abstract

This dataset contains many indicators in education such as as Net enrolment rate in primary education, Ratio of girls to boys in primary education, etc. The whole list and their description can be find in this link https://bit.ly/2NWP6Z1

Each row in this dataset represents tabular records. Data was last updated on HDX on 2024-09-13. Geographic scope: **ERI**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Education |
| **Unit of observation** | Tabular records |
| **Rows (total)** | 49 |
| **Columns** | 10 (7 numeric, 3 categorical, 0 datetime) |
| **Train split** | 39 rows |
| **Test split** | 9 rows |
| **Geographic scope** | ERI |
| **Publisher** | United Nations Economic Commission for Africa |
| **HDX last updated** | 2024-09-13 |

---

## Variables

**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-16).

**Other** — `indicator` (Adult literacy rate - Female (%), Net enrolment rate in secondary education - Female (%), Net enrolment rate in secondary education - Total (%)), `2010` (range 0.1–52.7), `2011` (range 0.8–55.5), `2012` (range 0.8–78.0), `2013` (range 1.0–91.8) and 3 others.

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-eritrea-uneca-education")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `indicator` | object | 0.0% | Adult literacy rate - Female (%), Net enrolment rate in secondary education - Female (%), Net enrolment rate in secondary education - Total (%) |
| `2010` | float64 | 26.5% | 0.1 – 52.7 (mean 15.2111) |
| `2011` | float64 | 36.7% | 0.8 – 55.5 (mean 21.6258) |
| `2012` | float64 | 30.6% | 0.8 – 78.0 (mean 27.9059) |
| `2013` | float64 | 34.7% | 1.0 – 91.8 (mean 31.0906) |
| `2014` | float64 | 22.4% | 0.1 – 74.4 (mean 23.8026) |
| `2015` | float64 | 22.4% | 0.1 – 93.3 (mean 26.2447) |
| `2017` | float64 | 57.1% | 2.1 – 53.1 (mean 26.8524) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-16 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `2010` | 0.1 | 52.7 | 15.2111 | 2.7 |
| `2011` | 0.8 | 55.5 | 21.6258 | 26.4 |
| `2012` | 0.8 | 78.0 | 27.9059 | 29.35 |
| `2013` | 1.0 | 91.8 | 31.0906 | 32.35 |
| `2014` | 0.1 | 74.4 | 23.8026 | 14.45 |
| `2015` | 0.1 | 93.3 | 26.2447 | 22.5 |
| `2017` | 2.1 | 53.1 | 26.8524 | 30.8 |

---

## 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`. 3 column(s) with >80% missing values were removed: `2016`, `2018`, `2019`. 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 United Nations Economic Commission 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.
- The following columns have >20% missing values and should be treated with caution in modelling: `2010`, `2011`, `2012`, `2013`, `2014`, `2015`, `2017`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/eritrea-uneca-education) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_eritrea_uneca_education,
  title     = {ERITREA - Education indicators, UNECA},
  author    = {United Nations Economic Commission for Africa},
  year      = {2024},
  url       = {https://data.humdata.org/dataset/eritrea-uneca-education},
  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.*