--- 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 pretty_name: "Employment in Large Establishments by Major Industrial Group and by Gender | Africa (Mauritius official open data)" --- # Employment in Large Establishments by Major Industrial Group and by Gender | Africa (Mauritius official open data) 864 rows - 1 Africa country - 2007-2022 - Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica) ![rows](https://img.shields.io/badge/rows-864-blue) ![countries](https://img.shields.io/badge/countries-1-green) ![years](https://img.shields.io/badge/years-2007-2022-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:** [Employment in Large Establishments by Major Industrial Group and by Gender](https://data.govmu.org/dataset/employment-large-establishments-major-industrial-group-and-gender) - **Publisher:** MDPA - **Resource:** [Employment-in-Large-Establishments-by-Major-Industrial-Group-and-by-Gender.csv](https://data.govmu.org/dataset/4e0520bb-551a-4bed-bfea-48ec05031eb9/resource/1a563147-aeee-4a24-af92-cfaa5050ec6b/download/employment-in-large-establishments-by-major-industrial-group-and-by-gender.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` | 864 | 2007 | 2022 | `Mauritius` | ## Indicators or Resource Contents - `employment-in-large-establishments-by-major-industrial-group-and-by-gend-33ae075e` - Employment in Large Establishments by Major Industrial Group and by Gender - no of male - `employment-in-large-establishments-by-major-industrial-group-and-by-gend-1a2bd720` - Employment in Large Establishments by Major Industrial Group and by Gender - no of female ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `indicator_id` | `object` | Stable indicator identifier. | `employment-in-large-establishments-by-major-industrial-group-and-by-gend` | | `indicator_name` | `object` | Human-readable indicator name. | `Employment in Large Establishments by Major Industrial Group and by Gend` | | `country_iso3` | `object` | ISO3 country code. | `MUS` | | `country_name` | `object` | Country name. | `Mauritius` | | `year` | `Int64` | Observation year. | `2007` | | `value` | `float64` | Numeric observation value. | `17711.0` | | `unit` | `object` | Measurement unit, when available. | `source_units_unspecified` | | `dimension_industrial_group` | `string` | Source dimension. | `Agriculture and Forestry and Fishing` | | `dimension_category` | `string` | Source dimension. | `` | | `source_provider` | `object` | Publishing organization. | `MDPA` | | `source_dataset` | `object` | Source package title. | `Employment in Large Establishments by Major Industrial Group and by Gend` | | `source_resource` | `object` | Source resource title. | `Employment-in-Large-Establishments-by-Major-Industrial-Group-and-by-Gend` | | `source_package_id` | `object` | CKAN package UUID. | `4e0520bb-551a-4bed-bfea-48ec05031eb9` | | `source_resource_id` | `object` | CKAN resource UUID. | `1a563147-aeee-4a24-af92-cfaa5050ec6b` | | `source_url` | `object` | Original CSV URL. | `https://data.govmu.org/dataset/4e0520bb-551a-4bed-bfea-48ec05031eb9/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-employment-in-large-establishments-by-major-industrial-gro-7ddfaee7") 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_employment_in_large_establishments_by_major_industrial_gro_7ddf_2022, title = {Employment in Large Establishments by Major Industrial Group and by Gender | Africa (Mauritius official open data)}, author = {MDPA}, year = {2022}, url = {https://data.govmu.org/dataset/employment-large-establishments-major-industrial-group-and-gender}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-employment-in-large-establishments-by-major-industrial-gro-7ddfaee7}} } ``` ## 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/4e0520bb-551a-4bed-bfea-48ec05031eb9/resource/1a563147-aeee-4a24-af92-cfaa5050ec6b/download/employment-in-large-establishments-by-major-industrial-group-and-by-gender.csv