electricsheepafrica/africa-mauritius-pre-primary-enrolment-and-gross-enrolment-ratio-by-gender-cb3db8fa

Pre Primary Enrolment and Gross Enrolment Ratio By Gender for Republic of Mauritius 1996 to 2021 | Africa (Mauritius official open data)

150 rows - 1 Africa country - 1996-2021 - Repackaged by Electric Sheep Africa

TL;DR

This dataset packages one official CSV resource from Mauritius as ML-ready Parquet. The CSV is the provenance boundary; all usable indicators or tabular columns from the source file stay together in this repo.

About the source

• Source: Pre Primary Enrolment and Gross Enrolment Ratio By Gender for Republic of Mauritius 1996 to 2021
• Publisher: MDPA
• Resource: Pre-Primary-Enrolment-and-Gross-Enrolment-Ratio-4-5-Years-By-Gender.csv
• License: CC BY-SA 4.0
• Packaging mode: indicator_long

Geographic coverage

1 Africa country:

Country	Rows	First year	Last year	Name
MUS	150	1996	2021	Mauritius

Indicators or Resource Contents

• pre-primary-enrolment-and-gross-enrolment-ratio-by-gender-for-republic-o-ced682b6 - Pre Primary Enrolment and Gross Enrolment Ratio By Gender for Republic of Mauritius 1996 to 2021 - male enrolment
• pre-primary-enrolment-and-gross-enrolment-ratio-by-gender-for-republic-o-092051e4 - Pre Primary Enrolment and Gross Enrolment Ratio By Gender for Republic of Mauritius 1996 to 2021 - female enrolment
• pre-primary-enrolment-and-gross-enrolment-ratio-by-gender-for-republic-o-177461d4 - Pre Primary Enrolment and Gross Enrolment Ratio By Gender for Republic of Mauritius 1996 to 2021 - male population
• pre-primary-enrolment-and-gross-enrolment-ratio-by-gender-for-republic-o-84a34f3c - Pre Primary Enrolment and Gross Enrolment Ratio By Gender for Republic of Mauritius 1996 to 2021 - female population
• pre-primary-enrolment-and-gross-enrolment-ratio-by-gender-for-republic-o-b1a14615 - Pre Primary Enrolment and Gross Enrolment Ratio By Gender for Republic of Mauritius 1996 to 2021 - ger male
• pre-primary-enrolment-and-gross-enrolment-ratio-by-gender-for-republic-o-a7c3e126 - Pre Primary Enrolment and Gross Enrolment Ratio By Gender for Republic of Mauritius 1996 to 2021 - ger female

Schema

Column	Type	Description	Example
indicator_id	object	Stable indicator identifier.	pre-primary-enrolment-and-gross-enrolment-ratio-by-gender-for-republic-o
indicator_name	object	Human-readable indicator name.	Pre Primary Enrolment and Gross Enrolment Ratio By Gender for Republic o
country_iso3	object	ISO3 country code.	MUS
country_name	object	Country name.	Mauritius
year	Int64	Observation year.	1996
value	float64	Numeric observation value.	17463.0
unit	object	Measurement unit, when available.	source_units_unspecified
source_provider	object	Publishing organization.	MDPA
source_dataset	object	Source package title.	Pre Primary Enrolment and Gross Enrolment Ratio By Gender for Republic o
source_resource	object	Source resource title.	Pre-Primary-Enrolment-and-Gross-Enrolment-Ratio-4-5-Years-By-Gender.csv
source_package_id	object	CKAN package UUID.	cdc94902-2b37-4e89-9293-3f73f6fd3bb9
source_resource_id	object	CKAN resource UUID.	779360d1-4c80-4db6-9d44-b8a9cef87d88
source_url	object	Original CSV URL.	https://data.govmu.org/dataset/cdc94902-2b37-4e89-9293-3f73f6fd3bb9/reso
license_id	object	Source license identifier.	CC-BY-SA-4.0
retrieved_at	object	UTC retrieval timestamp.	2026-07-16T19:23:24Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-pre-primary-enrolment-and-gross-enrolment-ratio-by-gender-cb3db8fa")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

sample_country = df[df["country_iso3"] == "MUS"]

Work with indicators

if "indicator_id" in df.columns:
    print(df["indicator_id"].value_counts().head())
    sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns])

Citation

@misc{electric_sheep_africa_africa_mauritius_pre_primary_enrolment_and_gross_enrolment_ratio_by_gender_cb3db_2021,
  title        = {Pre Primary Enrolment and Gross Enrolment Ratio By Gender for Republic of Mauritius 1996 to 2021 | Africa (Mauritius official open data)},
  author       = {MDPA},
  year         = {2021},
  url          = {https://data.govmu.org/dataset/pre-primary-enrolment-and-gross-enrolment-ratio-4-5-years-gender-mauritius},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-pre-primary-enrolment-and-gross-enrolment-ratio-by-gender-cb3db8fa}}
}

License

Released under CC BY-SA 4.0.

Original data (c) MDPA. When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging.

About Electric Sheep

Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on Hugging Face. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepafrica

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Provenance: ingested 2026-07-16 via the Electric Sheep pipeline. Source URL: https://data.govmu.org/dataset/cdc94902-2b37-4e89-9293-3f73f6fd3bb9/resource/779360d1-4c80-4db6-9d44-b8a9cef87d88/download/pre-primary-enrolment-and-gross-enrolment-ratio-4-5-years-by-gender.csv