electricsheepafrica/africa-mauritius-direct-employment-movement-for-locals-9d3321d4

Direct Employment Movement for Locals | Africa (MDPA)

8 rows - 1 Africa country/area - 2022 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 8 rows from MDPA, covering Direct Employment Movement for Locals. 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: CSV File

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	8
Countries/areas	1
First period	2022
Last period	2022
Indicators	0
Columns	22
Source format	CSV

Geographic Coverage

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

Area	Rows	First year	Last year	Name
MU	8	2022	2022	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.	71f98b48-d998-4935-9cac-627173797b40:0
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
year	int64	Observation year.	2022
unnamed_0	string	Source column from the original resource.	Employment as at 30 Jun 22
managerial_male	int64	Source column from the original resource.	968
managerial_female	string	Source column from the original resource.	674
technical_male	string	Source column from the original resource.	1,754
technical_female	int64	Source column from the original resource.	3430
support_male	int64	Source column from the original resource.	994
support_female	int64	Source column from the original resource.	2005
source_period_start_year	int64	Start year inferred from source metadata.	2022
source_period_end_year	int64	End year inferred from source metadata.	2022
source_period_label	dictionary<values=string, indices=int8, ordered=0>	Source column from the original resource.	2022
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.	Direct Employment movement for locals
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	local.csv
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	ce21aadc-bf19-4cbf-aa87-610a9095f3e5
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	71f98b48-d998-4935-9cac-627173797b40
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://data.govmu.org/dataset/ce21aadc-bf19-4cbf-aa87-610a9095f3e5/r...
license_id	dictionary<values=string, indices=int8, ordered=0>	Source license identifier.	odc-by
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-direct-employment-movement-for-locals-9d3321d4")
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: local.csv
• License: Open Data Commons Attribution License
• Retrieved/generated: 2026-08-08T16:56:17Z
• Hugging Face repo: electricsheepafrica/africa-mauritius-direct-employment-movement-for-locals-9d3321d4

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

Citation

@misc{electric_sheep_africa_africa_mauritius_direct_employment_movement_for_locals_9d3321d4_2022,
  title        = {Direct Employment Movement for Locals | Africa (MDPA)},
  author       = {MDPA},
  year         = {2022},
  url          = {https://data.govmu.org/dataset/direct-employment-movement-for-locals},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-direct-employment-movement-for-locals-9d3321d4}}
}

License

Released under Open Data Commons Attribution License.

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/direct-employment-movement-for-locals