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electricsheepafrica/africa-mauritius-enrolment-by-course-level-mode-of-study-by-gender-and-by-y-51a75d79

Enrolment by Course Level Mode of Study by Gender and by Y | Africa (MDPA) 151 rows - 1 Africa country/area - 2017-2022 - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 151 rows from MDPA, covering Enrolment by Course Level Mode of Study by Gender and by Y. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples. What This Dataset Measures… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-enrolment-by-course-level-mode-of-study-by-gender-and-by-y-51a75d79.

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Enrolment by Course Level Mode of Study by Gender and by Y | Africa (MDPA)

151 rows - 1 Africa country/area - 2017-2022 - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 151 rows from MDPA, covering Enrolment by Course Level Mode of Study by Gender and by Y. 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: Dataset Shows the Number of Participant Enrolled by Course Level, Mode of Study, by Gender and by Year for Mauritius Institute of Education for the Year 2017 to 2022.

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: not detected.
  • —Time coverage basis: source metadata.
  • —Recommended join keys: country_iso3 where available plus source-specific keys.

Coverage

DimensionValue
Rows151
Countries/areas1
First period2017
Last period2022
Indicators0
Columns60
Source formatXLSX

Geographic Coverage

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

AreaRowsFirst yearLast yearName
MU15120172022Mauritius

Indicators, Variables, Or Resource Contents

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

Schema

ColumnTypeDescriptionExample
source_record_idstringStable row identifier assigned during Electric Sheep Africa engineering.ce10ee97-b54e-4c92-b8a4-e7bb62dea178:2017-2018:0
country_iso3dictionary<values=string, indices=int8, ordered=0>ISO3 country or area code.MU
country_namedictionary<values=string, indices=int8, ordered=0>Country or area name.Mauritius
source_sheetstringSource column from the original resource.2017-2018
post_graduate_certificate_in_educationstringSource column from the original resource.Bachelor in Education 1
d_26doubleSource column from the original resource.20.0
d_160doubleSource column from the original resource.82.0
d_186doubleSource column from the original resource.102.0
d_159doubleSource column from the original resource.27.0
d_336doubleSource column from the original resource.17.0
d_495doubleSource column from the original resource.44.0
d_185doubleSource column from the original resource.47.0
d_496doubleSource column from the original resource.99.0
d_681doubleSource column from the original resource.146.0
source_period_start_yearint64Start year inferred from source metadata.2017
source_period_end_yearint64End year inferred from source metadata.2022
source_period_labeldictionary<values=string, indices=int8, ordered=0>Source column from the original resource.2017-2022
source_providerdictionary<values=string, indices=int8, ordered=0>Publishing organization.MDPA
source_datasetdictionary<values=string, indices=int8, ordered=0>Source dataset or package title.Enrolment by Course Level, Mode of Study, by Gender and by Year for M...
source_resourcedictionary<values=string, indices=int8, ordered=0>Source resource title, table name, or file name.Source-File_44.xlsx
source_package_iddictionary<values=string, indices=int8, ordered=0>Source package identifier.dc32430f-3ed3-47ac-b75c-fb13786bd196
source_resource_iddictionary<values=string, indices=int8, ordered=0>Source resource identifier.ce10ee97-b54e-4c92-b8a4-e7bb62dea178
source_urldictionary<values=string, indices=int8, ordered=0>Original source URL or download URL.https://data.govmu.org/dataset/dc32430f-3ed3-47ac-b75c-fb13786bd196/r...
license_iddictionary<values=string, indices=int8, ordered=0>Source license identifier.CC-BY-SA-4.0
retrieved_atdictionary<values=string, indices=int8, ordered=0>UTC source retrieval timestamp from the Electric Sheep Africa pipeline.2026-08-08T16:26:20Z
d_33stringSource column from the original resource.``
d_167stringSource column from the original resource.``
d_200stringSource column from the original resource.``
d_150stringSource column from the original resource.``
d_365stringSource column from the original resource.``
d_515stringSource column from the original resource.``
d_183stringSource column from the original resource.``
d_532doubleSource column from the original resource.``
d_715doubleSource column from the original resource.``
d_18doubleSource column from the original resource.``
d_138doubleSource column from the original resource.``
d_156doubleSource column from the original resource.``
d_115doubleSource column from the original resource.``
d_265doubleSource column from the original resource.``
d_380doubleSource column from the original resource.``
d_133doubleSource column from the original resource.``
d_403doubleSource column from the original resource.``
d_536doubleSource column from the original resource.``
post_graduate_certificatestringSource column from the original resource.``
d_9doubleSource column from the original resource.``
d_72doubleSource column from the original resource.``
d_81doubleSource column from the original resource.``
d_94doubleSource column from the original resource.``
d_249doubleSource column from the original resource.``
d_343doubleSource column from the original resource.``
d_103doubleSource column from the original resource.``
d_321doubleSource column from the original resource.``
d_424doubleSource column from the original resource.``
d_12doubleSource column from the original resource.``
d_84doubleSource column from the original resource.``
d_69doubleSource column from the original resource.``
d_235doubleSource column from the original resource.``
d_304doubleSource column from the original resource.``
d_307doubleSource column from the original resource.``
d_388doubleSource column from the original resource.``

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-enrolment-by-course-level-mode-of-study-by-gender-and-by-y-51a75d79")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

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

Filter By Geography

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

Time-Series Pattern

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

Pivot For Analysis

python
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

  • —No canonical year/date column was detected in the packaged table; use source metadata and domain context for temporal interpretation.
  • —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

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
  • —Check missingness before modeling
  • —Use country_iso3 as the safest geography join key when present

Citation

bibtex
@misc{electric_sheep_africa_africa_mauritius_enrolment_by_course_level_mode_of_study_by_gender_and_by_y_51a7_2022,
  title        = {Enrolment by Course Level Mode of Study by Gender and by Y | Africa (MDPA)},
  author       = {MDPA},
  year         = {2022},
  url          = {https://data.govmu.org/dataset/enrolment-course-level-mode-study-gender-and-year-mauritius-institute-education},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-enrolment-by-course-level-mode-of-study-by-gender-and-by-y-51a75d79}}
}

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.


Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://data.govmu.org/dataset/enrolment-course-level-mode-study-gender-and-year-mauritius-institute-education