electricsheepafrica/africa-ethiopian-primary-secondary-school-population-by-gender

---
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:
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- children
- education
- eth
pretty_name: "Ethiopian - Primary & Secondary School Population by Gender"
dataset_info:
splits:
- name: train
num_examples: 865
- name: test
num_examples: 216
---

# Ethiopian - Primary & Secondary School Population by Gender

**Publisher:** 3iS · **Source:** [HDX](https://data.humdata.org/dataset/ethiopian_primary-secondary_school_population_by_gender) · **License:** `cc-by` · **Updated:** 2025-04-15

---

## Abstract

This dataset contains the population distribution of primary and secondary schools in Ethiopia, disaggregated by sex.

Each row in this dataset represents subnational administrative unit observations. Temporal coverage is indicated by the `date`, `validon` column(s). Geographic scope: **ETH**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Education |
| **Unit of observation** | Subnational administrative unit observations |
| **Rows (total)** | 1,082 |
| **Columns** | 19 (7 numeric, 10 categorical, 2 datetime) |
| **Train split** | 865 rows |
| **Test split** | 216 rows |
| **Geographic scope** | ETH |
| **Publisher** | 3iS |
| **HDX last updated** | 2025-04-15 |

---

## Variables

**Geographic** — `admin3name` (Tahtay Adiyabo, Shilabo, Daror), `admin3pcod` (ET010101, ET050503, ET050392), `admin2name` (Sidama, North Shewa (AM), South Wello), `admin2pcod` (ET1600, ET0305, ET0304), `admin1name` (Oromia, Amhara, SNNP) and 7 others.

**Temporal** — `date`.

**Identifier / Metadata** — `objectid` (range 1.0–1082.0), `validon`, `esa_source` (HDX), `esa_processed` (2026-04-06).

**Other** — `shape_leng` (range 0.0264–5.59), `shape_area` (range 0.0–1.0433).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-ethiopian-primary-secondary-school-population-by-gender")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `objectid` | int64 | 0.0% | 1.0 – 1082.0 (mean 541.5) |
| `admin3name` | object | 0.0% | Tahtay Adiyabo, Shilabo, Daror |
| `admin3pcod` | object | 0.0% | ET010101, ET050503, ET050392 |
| `admin2name` | object | 0.0% | Sidama, North Shewa (AM), South Wello |
| `admin2pcod` | object | 0.0% | ET1600, ET0305, ET0304 |
| `admin1name` | object | 0.0% | Oromia, Amhara, SNNP |
| `admin1pcod` | object | 0.0% | ET04, ET03, ET07 |
| `admin0name` | object | 0.0% | Ethiopia |
| `admin0pcod` | object | 0.0% | ET |
| `date` | datetime64[ns] | 0.0% | |
| `validon` | datetime64[ns] | 0.0% | |
| `shape_leng` | float64 | 0.0% | 0.0264 – 5.59 (mean 1.4259) |
| `shape_area` | float64 | 0.0% | 0.0 – 1.0433 (mean 0.0856) |
| `male_secondary_school` | int64 | 0.0% | 16.0 – 29358.0 (mean 6659.9455) |
| `male_primary_school` | int64 | 0.0% | 20.0 – 29242.0 (mean 7706.086) |
| `female_primary_school` | int64 | 0.0% | 19.0 – 30415.0 (mean 7698.7357) |
| `female_secondary_school` | int64 | 0.0% | 19.0 – 43410.0 (mean 6722.7689) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-06 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `objectid` | 1.0 | 1082.0 | 541.5 | 541.5 |
| `shape_leng` | 0.0264 | 5.59 | 1.4259 | 1.3717 |
| `shape_area` | 0.0 | 1.0433 | 0.0856 | 0.048 |
| `male_secondary_school` | 16.0 | 29358.0 | 6659.9455 | 5899.0 |
| `male_primary_school` | 20.0 | 29242.0 | 7706.086 | 6946.5 |
| `female_primary_school` | 19.0 | 30415.0 | 7698.7357 | 6807.0 |
| `female_secondary_school` | 19.0 | 43410.0 | 6722.7689 | 5818.0 |

---

## 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`. 4 column(s) with >80% missing values were removed: `admin3refn`, `admin3altn`, `admin3al_1`, `validto`. 2 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 3iS 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/ethiopian_primary-secondary_school_population_by_gender) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_ethiopian_primary_secondary_school_population_by_gender,
title = {Ethiopian - Primary & Secondary School Population by Gender},
author = {3iS},
year = {2025},
url = {https://data.humdata.org/dataset/ethiopian_primary-secondary_school_population_by_gender},
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.*