electricsheepafrica/africa-malawi-health-facility-registry

Malawi - Health Facility Registry

Publisher: Health Cluster · Source: HDX · License: cc-by · Updated: 2025-05-05

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Abstract

The main purpose of the Malawi Health Facility Registry 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 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.

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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

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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.

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Quick Start

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()

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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

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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

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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.

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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 for the publisher's own methodology notes and caveats.

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Citation

@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)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.