electricsheepafrica/africa-mauritius-enrolment-in-special-education-needs-sen-schools-by-gender-629e6a1d

Enrolment in Special Education Needs Sen Schools by Gender | Africa (MDPA)

12 rows - 1 Africa country/area - 2019 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 12 rows from MDPA, covering Enrolment in Special Education Needs Sen Schools by Gender. 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 (SEN) schools by gender and district for the year 2016 - 2021

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: year.
• Time coverage basis: year.
• Recommended join keys: country_iso3 where available plus source-specific keys.

Coverage

Dimension	Value
Rows	12
Countries/areas	1
First period	2019
Last period	2019
Indicators	0
Columns	26
Source format	XLSX

Geographic Coverage

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

Area	Rows	First year	Last year	Name
MU	12	2019	2019	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.	0724be43-abb9-4443-9a14-22cee47bc4f5:t10-2:0
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.	T10.2
year	int64	Observation year.	2019
port_louis	string	Source column from the original resource.	Pamplemousses
d_309	double	Source column from the original resource.	185.0
d_179	double	Source column from the original resource.	130.0
d_130	double	Source column from the original resource.	55.0
d_305	double	Source column from the original resource.	175.0
d_182	double	Source column from the original resource.	121.0
d_123	double	Source column from the original resource.	54.0
d_239	double	Source column from the original resource.	179.0
d_137	double	Source column from the original resource.	127.0
d_102	double	Source column from the original resource.	52.0
source_period_start_year	int64	Start year inferred from source metadata.	2019
source_period_end_year	int64	End year inferred from source metadata.	2019
source_period_label	dictionary<values=string, indices=int8, ordered=0>	Source column from the original resource.	2019
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 Special Education Needs (SEN) schools by gender and dist...
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	Sourcefile_2019.xlsx
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	13642058-950c-4f39-a499-81479c2bb96d
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	0724be43-abb9-4443-9a14-22cee47bc4f5
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://data.govmu.org/dataset/13642058-950c-4f39-a499-81479c2bb96d/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-special-education-needs-sen-schools-by-gender-629e6a1d")
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 "year" in df.columns:
    trend = df.sort_values("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: 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: Sourcefile_2019.xlsx
• License: CC BY-SA 4.0
• Retrieved/generated: 2026-08-08T17:11:12Z
• Hugging Face repo: electricsheepafrica/africa-mauritius-enrolment-in-special-education-needs-sen-schools-by-gender-629e6a1d

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_special_education_needs_sen_schools_by_gender_629e_2019,
  title        = {Enrolment in Special Education Needs Sen Schools by Gender | Africa (MDPA)},
  author       = {MDPA},
  year         = {2019},
  url          = {https://data.govmu.org/dataset/enrolment-special-education-needs-sen-schools-gender-and-district},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-enrolment-in-special-education-needs-sen-schools-by-gender-629e6a1d}}
}

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-special-education-needs-sen-schools-gender-and-district