electricsheepafrica/africa-mauritius-expatriate-employment-by-product-group-and-sex-eoe-sector-19ba991a

Expatriate Employment by Product Group and Sex Eoe Sector | Africa (MDPA)

260 rows - 1 Africa country/area - 2011-2020 - 2 indicators - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

TL;DR

This dataset contains 260 rows from MDPA, covering Expatriate Employment by Product Group and Sex Eoe Sector. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Labour and workforce datasets help analysts study employment, participation, skills, sectoral structure, and the movement of people through work and livelihoods.

Source-provided context: Dataset shows the Expatriate employment by product group and sex, Export Oriented Enterprise Sector from March 2011 to March 2020

How To Read This Dataset

• —One row means: one indicator observation for one geography, time period, and optional source dimensions.
• —Primary geography column: country_iso3.
• —Best time column: year.
• —Time coverage basis: year.
• —Recommended join keys: country_iso3, year, indicator_id.

Coverage

Geographic Coverage

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

Indicators, Variables, Or Resource Contents

• —expatriate-employment-by-product-group-and-sex-eoe-sector-march-2011-mar-78f4e4b2 - Expatriate employment by product group and sex, EOE Sector March 2011 - March 2020 - male(sourceunitsunspecified)
• —expatriate-employment-by-product-group-and-sex-eoe-sector-march-2011-mar-9a0a2ea7 - Expatriate employment by product group and sex, EOE Sector March 2011 - March 2020 - female(sourceunitsunspecified)

Schema

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-expatriate-employment-by-product-group-and-sex-eoe-sector-19ba991a")
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: Expatriate employment by product group and sex, EOE Sector 2011 to 2020.csv
• —License: CC BY 4.0
• —Retrieved/generated: 2026-08-08T16:53:21Z
• —Hugging Face repo: electricsheepafrica/africa-mauritius-expatriate-employment-by-product-group-and-sex-eoe-sector-19ba991a

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

• —Track workforce composition over time
• —Compare employment patterns across groups
• —Join with education, population, and sector data
• —Build time-series views and period-over-period comparisons
• —Pivot to geography x period or indicator x period matrices
• —Check missingness before modeling
• —Use country_iso3 as the safest geography join key when present

Citation

@misc{electric_sheep_africa_africa_mauritius_expatriate_employment_by_product_group_and_sex_eoe_sector_19ba9_2020,
  title        = {Expatriate Employment by Product Group and Sex Eoe Sector | Africa (MDPA)},
  author       = {MDPA},
  year         = {2020},
  url          = {https://data.govmu.org/dataset/expatriate-employment-by-product-group-and-sex-eoe-sector-march-2011-march-2020},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-expatriate-employment-by-product-group-and-sex-eoe-sector-19ba991a}}
}

License

Released under CC BY 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/expatriate-employment-by-product-group-and-sex-eoe-sector-march-2011-march-2020