electricsheepafrica/africa-zimbabwe-schools-in-zimbabwe

---
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
- education
- education-facilities-schools
- facilities-infrastructure
- hxl
- zwe
pretty_name: "Zimbabwe: Schools"
dataset_info:
splits:
- name: train
num_examples: 7823
- name: test
num_examples: 1955
---

# Zimbabwe: Schools

**Publisher:** OCHA Regional Office for Southern and Eastern Africa (ROSEA) · **Source:** [HDX](https://data.humdata.org/dataset/zimbabwe-schools-in-zimbabwe) · **License:** `cc-by` · **Updated:** 2025-04-10

---

## Abstract

Schools and learning facilities in Zimbabwe

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2025-04-10. Geographic scope: **ZWE**.

*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)** | 9,779 |
| **Columns** | 10 (3 numeric, 7 categorical, 0 datetime) |
| **Train split** | 7,823 rows |
| **Test split** | 1,955 rows |
| **Geographic scope** | ZWE |
| **Publisher** | OCHA Regional Office for Southern and Eastern Africa (ROSEA) |
| **HDX last updated** | 2025-04-10 |

---

## Variables

**Geographic** — `province` (Manicaland, Midlands, Masvingo), `district` (Mutare, Makoni, Hurungwe), `latitude` (range -22.3313–0.0), `longitude` (range 0.0–33.0239).

**Outcome / Measurement** — `schoolnumber` (range 1001.0–45632.0).

**Identifier / Metadata** — `name` (KUSHINGA, RUSUNUNGUKO, BATANAI), `esa_source` (HDX), `esa_processed` (2026-04-18).

**Other** — `schoollevel` (Primary, Secondary, #loc+school+type), `grant_class` (P3, S3, P2).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-zimbabwe-schools-in-zimbabwe")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `schoolnumber` | float64 | 0.0% | 1001.0 – 45632.0 (mean 9070.2503) |
| `name` | object | 0.0% | KUSHINGA, RUSUNUNGUKO, BATANAI |
| `province` | object | 0.0% | Manicaland, Midlands, Masvingo |
| `schoollevel` | object | 0.0% | Primary, Secondary, #loc+school+type |
| `district` | object | 0.0% | Mutare, Makoni, Hurungwe |
| `grant_class` | object | 0.0% | P3, S3, P2 |
| `latitude` | float64 | 19.0% | -22.3313 – 0.0 (mean -18.8668) |
| `longitude` | float64 | 10.8% | 0.0 – 33.0239 (mean 27.6846) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-18 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `schoolnumber` | 1001.0 | 45632.0 | 9070.2503 | 8913.5 |
| `latitude` | -22.3313 | 0.0 | -18.8668 | -18.7803 |
| `longitude` | 0.0 | 33.0239 | 27.6846 | 30.5293 |

---

## 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) 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 OCHA Regional Office for Southern and Eastern Africa (ROSEA) 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/zimbabwe-schools-in-zimbabwe) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_zimbabwe_schools_in_zimbabwe,
title = {Zimbabwe: Schools},
author = {OCHA Regional Office for Southern and Eastern Africa (ROSEA)},
year = {2025},
url = {https://data.humdata.org/dataset/zimbabwe-schools-in-zimbabwe},
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.*