electricsheepafrica/africa-world-bank-health-indicators-for-mauritius

Mauritius - Health

Publisher: World Bank Group · Source: HDX · License: cc-by · Updated: 2026-03-27

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Abstract

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Improving health is central to the Millennium Development Goals, and the public sector is the main provider of health care in developing countries. To reduce inequities, many countries have emphasized primary health care, including immunization, sanitation, access to safe drinking water, and safe motherhood initiatives. Data here cover health systems, disease prevention, reproductive health, nutrition, and population dynamics. Data are from the United Nations Population Division, World Health Organization, United Nations Children's Fund, the Joint United Nations Programme on HIV/AIDS, and various other sources.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: MUS.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Public health
Unit of observation	Country-level aggregates
Rows (total)	7,630
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	6,104 rows
Test split	1,526 rows
Geographic scope	MUS
Publisher	World Bank Group
HDX last updated	2026-03-27

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Variables

Geographic — country_name (Mauritius), country_iso3 (MUS), year (range 1960.0–2025.0).

Outcome / Measurement — value (range -8984.0–1266334.0).

Identifier / Metadata — indicator_name (Net migration, Population ages 55-59, female (% of female population), Population ages 80 and above, male (% of male population)), indicator_code (SM.POP.NETM, SP.POP.5559.FE.5Y, SP.POP.80UP.MA.5Y), esa_source (HDX), esa_processed (2026-04-12).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-world-bank-health-indicators-for-mauritius")
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
country_name	object	0.0%	Mauritius
country_iso3	object	0.0%	MUS
year	int64	0.0%	1960.0 – 2025.0 (mean 1996.2893)
indicator_name	object	0.0%	Net migration, Population ages 55-59, female (% of female population), Population ages 80 and above, male (% of male population)
indicator_code	object	0.0%	SM.POP.NETM, SP.POP.5559.FE.5Y, SP.POP.80UP.MA.5Y
value	float64	0.0%	-8984.0 – 1266334.0 (mean 36150.1851)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-12

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Numeric Summary

Column	Min	Max	Mean	Median
year	1960.0	2025.0	1996.2893	2000.0
value	-8984.0	1266334.0	36150.1851	17.9101

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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. 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 World Bank Group 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_world_bank_health_indicators_for_mauritius,
  title     = {Mauritius - Health},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-health-indicators-for-mauritius},
  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.