---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- cyclones-hurricanes-typhoons
- geodata
- health-facilities
- moz
pretty_name: "Mozambique - Health facilities"
dataset_info:
  splits:
    - name: train
      num_examples: 58
    - name: test
      num_examples: 14
---

# Mozambique - Health facilities

**Publisher:** OCHA Mozambique · **Source:** [HDX](https://data.humdata.org/dataset/mozambique-health-facilities) · **License:** `other-pd-nr` · **Updated:** 2025-04-08

---

## Abstract

Mozambique health facilities

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-04-08. Geographic scope: **MOZ**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Public health |
| **Unit of observation** | Tabular records |
| **Rows (total)** | 73 |
| **Columns** | 2 (0 numeric, 2 categorical, 0 datetime) |
| **Train split** | 58 rows |
| **Test split** | 14 rows |
| **Geographic scope** | MOZ |
| **Publisher** | OCHA Mozambique |
| **HDX last updated** | 2025-04-08 |

---

## Variables

**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-12).

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-mozambique-health-facilities")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-12 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
_No numeric columns._

---

## 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`. 23 column(s) with >80% missing values were removed: `unnamed_0`, `unnamed_1`, `unnamed_2`, `unnamed_3`, `unnamed_4`, `unnamed_5`.... 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 Mozambique 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/mozambique-health-facilities) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_mozambique_health_facilities,
  title     = {Mozambique - Health facilities},
  author    = {OCHA Mozambique},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/mozambique-health-facilities},
  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.*