---
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-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- facilities-infrastructure
- health
- health-facilities
- mwi
pretty_name: "Malawi - Health Facility Registry"
dataset_info:
  splits:
    - name: train
      num_examples: 25
    - name: test
      num_examples: 6
---

# Malawi - Health Facility Registry

**Publisher:** Health Cluster · **Source:** [HDX](https://data.humdata.org/dataset/malawi-health-facility-registry) · **License:** `cc-by` · **Updated:** 2025-05-05

---

## Abstract

The main purpose of the [Malawi Health Facility Registry](https://zipatala.health.gov.mw/) is to provide an easily accessible national online database containing a comprehensive list of health facilities in the country for purposes of facilitating planning for service delivery and performance accountability. The Ministry of Health and Population maintains a comprehensive list of all health facilities in Malawi with a purpose of strengthening health service delivery. The list was previously maintained through a spreadsheet file which made it difficult to update and to access especially by the decision makers.

Additionally, various sections of the Ministry have been maintaining parallel health facility lists making it harder to share and triangulate data across systems. To address these challenges, the Ministry has implemented an online Master Health Facility Registry (MHFR). The MHFR is a web service with a user interface to allow health workers get access to the full list of facilities and view summaries through the dashboard in addition to performing custom queries depending of specific information

Please consult the [official website](https://zipatala.health.gov.mw/) to get the most updated database

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-05-05. Geographic scope: **MWI**.

*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)** | 32 |
| **Columns** | 6 (3 numeric, 3 categorical, 0 datetime) |
| **Train split** | 25 rows |
| **Test split** | 6 rows |
| **Geographic scope** | MWI |
| **Publisher** | Health Cluster |
| **HDX last updated** | 2025-05-05 |

---

## Variables

**Identifier / Metadata** — `unnamed_0` (Cuenta de CODE, Mulanje, Zomba), `unnamed_2` (range 1.0–351.0), `unnamed_3` (range 2.0–1162.0), `unnamed_4` (range 4.0–1514.0), `esa_source` (HDX) and 1 others.

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-malawi-health-facility-registry")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `unnamed_0` | object | 3.1% | Cuenta de CODE, Mulanje, Zomba |
| `unnamed_2` | float64 | 18.8% | 1.0 – 351.0 (mean 27.0) |
| `unnamed_3` | float64 | 9.4% | 2.0 – 1162.0 (mean 80.1379) |
| `unnamed_4` | float64 | 9.4% | 4.0 – 1514.0 (mean 104.4138) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-17 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `unnamed_2` | 1.0 | 351.0 | 27.0 | 9.5 |
| `unnamed_3` | 2.0 | 1162.0 | 80.1379 | 37.0 |
| `unnamed_4` | 4.0 | 1514.0 | 104.4138 | 43.0 |

---

## 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`. 1 column(s) with >80% missing values were removed: `unnamed_1`. 3 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 Health Cluster 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/malawi-health-facility-registry) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_malawi_health_facility_registry,
  title     = {Malawi - Health Facility Registry},
  author    = {Health Cluster},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/malawi-health-facility-registry},
  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.*