electricsheepafrica/africa-hdro-data-for-mauritius

Mauritius - Human Development Indicators

Publisher: UNDP Human Development Reports Office (HDRO) · Source: HDX · License: cc-by-igo · Updated: 2026-03-04

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Abstract

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-04. 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)	889
Columns	10 (2 numeric, 8 categorical, 0 datetime)
Train split	711 rows
Test split	177 rows
Geographic scope	MUS
Publisher	UNDP Human Development Reports Office (HDRO)
HDX last updated	2026-03-04

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Variables

Geographic — country_code (MUS), country_name (Mauritius), index_id (GDI, GII, HDI), index_name (Gender Development Index, Gender Inequality Index, Human Development Index), year (range 1990.0–2023.0).

Outcome / Measurement — value (range 0.586–37829.368).

Identifier / Metadata — indicator_id (abr, co2_prod, se_f), indicator_name (Adolescent Birth Rate (births per 1,000 women ages 15-19), Carbon dioxide emissions per capita (production) (tonnes), Population with at least some secondary education, female (% ages 25 and older)), esa_source (HDX), esa_processed (2026-04-06).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-hdro-data-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_code	object	0.0%	MUS
country_name	object	0.0%	Mauritius
indicator_id	object	0.0%	abr, co2_prod, se_f
indicator_name	object	0.0%	Adolescent Birth Rate (births per 1,000 women ages 15-19), Carbon dioxide emissions per capita (production) (tonnes), Population with at least some secondary education, female (% ages 25 and older)
index_id	object	0.0%	GDI, GII, HDI
index_name	object	0.0%	Gender Development Index, Gender Inequality Index, Human Development Index
value	float64	0.0%	0.586 – 37829.368 (mean 2018.1664)
year	int64	0.0%	1990.0 – 2023.0 (mean 2007.3431)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-06

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Numeric Summary

Column	Min	Max	Mean	Median
value	0.586	37829.368	2018.1664	27.504
year	1990.0	2023.0	2007.3431	2008.0

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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. 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 UNDP Human Development Reports Office (HDRO) 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_hdro_data_for_mauritius,
  title     = {Mauritius - Human Development Indicators},
  author    = {UNDP Human Development Reports Office (HDRO)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/hdro-data-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.