---
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
- health-facilities
- hxl
- zwe
pretty_name: "Zimbabwe Healthsites"
dataset_info:
  splits:
    - name: train
      num_examples: 755
    - name: test
      num_examples: 188
---

# Zimbabwe Healthsites

**Publisher:** Global Healthsites Mapping Project · **Source:** [HDX](https://data.humdata.org/dataset/zimbabwe-healthsites) · **License:** `ODbL` · **Updated:** 2025-04-15

---

## Abstract

This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-04-15. Geographic scope: **ZWE**.

*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)** | 944 |
| **Columns** | 13 (6 numeric, 6 categorical, 0 datetime) |
| **Train split** | 755 rows |
| **Test split** | 188 rows |
| **Geographic scope** | ZWE |
| **Publisher** | Global Healthsites Mapping Project |
| **HDX last updated** | 2025-04-15 |

---

## Variables

**Geographic** — `x` (range 25.837–32.9726), `y` (range -22.2136–-15.6681), `osm_type` (node, way), `loc_amenity` (clinic, hospital, pharmacy).

**Temporal** — `changeset_timestamp`.

**Identifier / Metadata** — `osm_id` (range -12600896.0–12480272510.0), `loc_name` (Clinic, Rural Health Care Centre, Gumbo Clinic), `changeset_id` (range 5988946.0–164035674.0), `meta_id` (7f9263e10dc147029bb8a372a615d92d, 39cc35b5b8ef4be794bacf05c9a763ca, 74645d4aaec0483c8738dc655e6e6788), `esa_source` (HDX) and 1 others.

**Other** — `completeness` (range 6.25–34.375), `changeset_version` (range 1.0–8.0).

---

## Quick Start

```python
from datasets import load_dataset

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

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `x` | float64 | 36.3% | 25.837 – 32.9726 (mean 31.0282) |
| `y` | float64 | 36.3% | -22.2136 – -15.6681 (mean -18.3389) |
| `osm_id` | int64 | 0.0% | -12600896.0 – 12480272510.0 (mean 5784919390.7299) |
| `osm_type` | object | 0.0% | node, way |
| `completeness` | float64 | 0.0% | 6.25 – 34.375 (mean 10.5667) |
| `loc_amenity` | object | 1.1% | clinic, hospital, pharmacy |
| `loc_name` | object | 5.4% | Clinic, Rural Health Care Centre, Gumbo Clinic |
| `changeset_id` | float64 | 0.6% | 5988946.0 – 164035674.0 (mean 101901878.4051) |
| `changeset_version` | float64 | 0.6% | 1.0 – 8.0 (mean 1.5448) |
| `changeset_timestamp` | datetime64[ns, UTC] | 0.6% |  |
| `meta_id` | object | 0.0% | 7f9263e10dc147029bb8a372a615d92d, 39cc35b5b8ef4be794bacf05c9a763ca, 74645d4aaec0483c8738dc655e6e6788 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-20 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `x` | 25.837 | 32.9726 | 31.0282 | 31.1257 |
| `y` | -22.2136 | -15.6681 | -18.3389 | -17.928 |
| `osm_id` | -12600896.0 | 12480272510.0 | 5784919390.7299 | 6202614917.5 |
| `completeness` | 6.25 | 34.375 | 10.5667 | 9.375 |
| `changeset_id` | 5988946.0 | 164035674.0 | 101901878.4051 | 124558102.0 |
| `changeset_version` | 1.0 | 8.0 | 1.5448 | 1.0 |

---

## 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`. 24 column(s) with >80% missing values were removed: `meta_healthcare`, `meta_operator`, `geo_bounds_url`, `meta_speciality`, `meta_operator_type`, `contact_phone`.... 1 exact duplicate rows were removed. 1 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 Global Healthsites Mapping Project 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: `x`, `y`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/zimbabwe-healthsites) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_health_facilities_zimbabwe,
  title     = {Zimbabwe Healthsites},
  author    = {Global Healthsites Mapping Project},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/zimbabwe-healthsites},
  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.*