--- license: cc-by-sa-4.0 language: - en task_categories: - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - n<1K tags: - tabular - csv - africa - mauritius - official-statistics - open-data - education - ict pretty_name: "Enrolment in Special Education Needs (SEN) schools by gender and district | Africa (Mauritius official open data)" --- # Enrolment in Special Education Needs (SEN) schools by gender and district | Africa (Mauritius official open data) 120 rows - 1 Africa country - 2016-2021 - Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica) ![rows](https://img.shields.io/badge/rows-120-blue) ![countries](https://img.shields.io/badge/countries-1-green) ![years](https://img.shields.io/badge/years-2016-2021-orange) ![indicators](https://img.shields.io/badge/indicators-2-purple) ![license](https://img.shields.io/badge/license-cc-by-sa-4.0-lightgrey) ## 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:** [Enrolment in Special Education Needs (SEN) schools by gender and district](https://data.govmu.org/dataset/enrolment-special-education-needs-sen-schools-gender-and-district) - **Publisher:** MDPA - **Resource:** [SEN.csv](https://data.govmu.org/dataset/13642058-950c-4f39-a499-81479c2bb96d/resource/cfb8ebdd-905d-4f46-9441-c2ecf1a9167d/download/sen.csv) - **License:** [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) - **Packaging mode:** `indicator_long` ## Geographic coverage 1 Africa country: | Country | Rows | First year | Last year | Name | |---------|-----:|-----------:|----------:|------| | `MUS` | 120 | 2016 | 2021 | `Mauritius` | ## Indicators or Resource Contents - `enrolment-in-special-education-needs-sen-schools-by-gender-and-district-af9690b5` - Enrolment in Special Education Needs (SEN) schools by gender and district - male - `enrolment-in-special-education-needs-sen-schools-by-gender-and-district-b9e67204` - Enrolment in Special Education Needs (SEN) schools by gender and district - female ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `indicator_id` | `object` | Stable indicator identifier. | `enrolment-in-special-education-needs-sen-schools-by-gender-and-district-` | | `indicator_name` | `object` | Human-readable indicator name. | `Enrolment in Special Education Needs (SEN) schools by gender and distric` | | `country_iso3` | `object` | ISO3 country code. | `MUS` | | `country_name` | `object` | Country name. | `Mauritius` | | `year` | `Int64` | Observation year. | `2016` | | `value` | `float64` | Numeric observation value. | `164.0` | | `unit` | `object` | Measurement unit, when available. | `source_units_unspecified` | | `dimension_island` | `string` | Source dimension. | `Island of Mauritius` | | `dimension_district` | `string` | Source dimension. | `Port Louis` | | `source_provider` | `object` | Publishing organization. | `MDPA` | | `source_dataset` | `object` | Source package title. | `Enrolment in Special Education Needs (SEN) schools by gender and distric` | | `source_resource` | `object` | Source resource title. | `SEN.csv` | | `source_package_id` | `object` | CKAN package UUID. | `13642058-950c-4f39-a499-81479c2bb96d` | | `source_resource_id` | `object` | CKAN resource UUID. | `cfb8ebdd-905d-4f46-9441-c2ecf1a9167d` | | `source_url` | `object` | Original CSV URL. | `https://data.govmu.org/dataset/13642058-950c-4f39-a499-81479c2bb96d/reso` | | `license_id` | `object` | Source license identifier. | `CC-BY-SA-4.0` | | `retrieved_at` | `object` | UTC retrieval timestamp. | `2026-07-16T19:23:24Z` | ## Usage ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-mauritius-enrolment-in-special-education-needs-sen-schools-by-gender-c1b3c078") df = ds["train"].to_pandas() print(df.head()) ``` ### Filter to one country ```python sample_country = df[df["country_iso3"] == "MUS"] ``` ### Work with indicators ```python 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 ```bibtex @misc{electric_sheep_africa_africa_mauritius_enrolment_in_special_education_needs_sen_schools_by_gender_c1b3_2021, title = {Enrolment in Special Education Needs (SEN) schools by gender and district | Africa (Mauritius official open data)}, author = {MDPA}, year = {2021}, url = {https://data.govmu.org/dataset/enrolment-special-education-needs-sen-schools-gender-and-district}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-enrolment-in-special-education-needs-sen-schools-by-gender-c1b3c078}} } ``` ## License Released under [CC BY-SA 4.0](https://creativecommons.org/licenses/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](https://huggingface.co/electricsheepafrica) --- Provenance: ingested 2026-07-16 via the Electric Sheep pipeline. Source URL: https://data.govmu.org/dataset/13642058-950c-4f39-a499-81479c2bb96d/resource/cfb8ebdd-905d-4f46-9441-c2ecf1a9167d/download/sen.csv