electricsheepafrica/africa-mauritius-enrolment-in-sen-schools-by-year-age-and-gender-for-republ-1659d61e

Enrolment in Sen Schools by Year Age and Gender for Republ | Africa (MDPA)

19 rows - 1 Africa country/area - detected - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 19 rows from MDPA, covering Enrolment in Sen Schools by Year Age and Gender for Republ. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Education datasets help analysts study access, participation, learning systems, infrastructure, and outcomes across places and periods.

Source-provided context: The data shows number of children enrolled in Special Education Needs school by year, age and gender

How To Read This Dataset

• One row means: one source record from the original tabular resource, with Electric Sheep Africa provenance columns added where available.
• Primary geography column: country_iso3.
• Best time column: source_period_start_year.
• Time coverage basis: source_period_start_year.
• Recommended join keys: country_iso3 where available plus source-specific keys.

Coverage

Dimension	Value
Rows	19
Countries/areas	1
First period	detected
Last period	detected
Indicators	0
Columns	25
Source format	XLSX

Geographic Coverage

Top areas shown below, sorted by row count when available:

Area	Rows	First year	Last year	Name
MU	19	detected	detected	Mauritius

Indicators, Variables, Or Resource Contents

• This repo preserves one source tabular resource with its usable columns kept together.

Schema

Column	Type	Description	Example
source_record_id	string	Stable row identifier assigned during Electric Sheep Africa engineering.	06c09c51-a5d6-410e-8323-9921375fad7b:t6-5:0
country_iso3	dictionary<values=string, indices=int8, ordered=0>	ISO3 country or area code.	MU
country_name	dictionary<values=string, indices=int8, ordered=0>	Country or area name.	Mauritius
source_sheet	string	Source column from the original resource.	T6.5
d_3	string	Source column from the original resource.	4
d_21	int64	Source column from the original resource.	19
d_17	int64	Source column from the original resource.	11
d_4	int64	Source column from the original resource.	8
d_11	int64	Source column from the original resource.	17
d_9	int64	Source column from the original resource.	10
d_2	int64	Source column from the original resource.	7
d_13	int64	Source column from the original resource.	18
d_7	int64	Source column from the original resource.	14
d_6	int64	Source column from the original resource.	4
source_period_start_year	int64	Start year inferred from source metadata.	``
source_period_end_year	int64	End year inferred from source metadata.	``
source_period_label	string	Source column from the original resource.	``
source_provider	dictionary<values=string, indices=int8, ordered=0>	Publishing organization.	MDPA
source_dataset	dictionary<values=string, indices=int8, ordered=0>	Source dataset or package title.	Enrolment in SEN schools by year, age and gender for Republic of Maur...
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	Source-file_1.xlsx
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	090ee43e-9ba8-40bd-a660-b60b3a1c097f
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	06c09c51-a5d6-410e-8323-9921375fad7b
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://data.govmu.org/dataset/090ee43e-9ba8-40bd-a660-b60b3a1c097f/r...
license_id	dictionary<values=string, indices=int8, ordered=0>	Source license identifier.	CC-BY-SA-4.0
retrieved_at	dictionary<values=string, indices=int8, ordered=0>	UTC source retrieval timestamp from the Electric Sheep Africa pipeline.	2026-08-08T16:26:20Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-enrolment-in-sen-schools-by-year-age-and-gender-for-republ-1659d61e")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

print(df.info())
print(df.head())

Filter By Geography

if "country_iso3" in df.columns:
    sample = df[df["country_iso3"] == "MU"]

Time-Series Pattern

if "value" in df.columns and "source_period_start_year" in df.columns:
    trend = df.sort_values("source_period_start_year")

Pivot For Analysis

if {"indicator_id", "year", "value"}.issubset(df.columns):
    matrix = df.pivot_table(index="year", columns="indicator_id", values="value")
    print(matrix.tail())

Data Quality Notes

• Canonical time field: source_period_start_year.
• Missing values are preserved rather than silently imputed.
• Column names are standardized for machine use; source meanings are preserved where known.
• Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use.

Source And Provenance

• Source: MDPA
• Publisher: MDPA
• Portal: https://data.govmu.org
• Resource: Source-file_1.xlsx
• License: CC BY-SA 4.0
• Retrieved/generated: 2026-08-08T16:37:04Z
• Hugging Face repo: electricsheepafrica/africa-mauritius-enrolment-in-sen-schools-by-year-age-and-gender-for-republ-1659d61e

Transformations Applied

• Converted the source table to Parquet for efficient analytics and ML workflows.
• Added or preserved source provenance columns where available.
• Standardized README metadata, dataset loading configuration, schema documentation, and citation format.
• Preserved source-reported values without analytical imputation.

Suggested Analyses

• Compare education indicators by geography
• Track participation or completion trends
• Join with population and poverty indicators
• Build time-series views and period-over-period comparisons
• Check missingness before modeling
• Use country_iso3 as the safest geography join key when present

Citation

@misc{electric_sheep_africa_africa_mauritius_enrolment_in_sen_schools_by_year_age_and_gender_for_republ_1659_2026,
  title        = {Enrolment in Sen Schools by Year Age and Gender for Republ | Africa (MDPA)},
  author       = {MDPA},
  year         = {2026},
  url          = {https://data.govmu.org/dataset/enrolment-sen-schools-year-age-and-gender-republic-mauritius},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-enrolment-in-sen-schools-by-year-age-and-gender-for-republ-1659d61e}}
}

License

Released under CC BY-SA 4.0.

Original data is published by MDPA. Electric Sheep Africa engineering standardizes the data for discovery, loading, and analysis on Hugging Face. Cite both the original source and this ML-ready dataset when used.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.

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Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://data.govmu.org/dataset/enrolment-sen-schools-year-age-and-gender-republic-mauritius