electricsheepafrica/africa-mauritius-private-households-by-geographical-location-and-availabili-db18dac9

Private Households by Geographical Location and Availabili | Africa (MDPA)

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

TL;DR

This dataset contains 213 rows from MDPA, covering Private Households by Geographical Location and Availabili. 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 Private Households by Geographical Location and Availability of TV, Refrigerator, Washing Machine, Gas or Electric Oven, Fixed Telephone Line, Mobile Phone, Computer or Laptop or Tablet for Mauritius, Rodrigues and Agalega for the Year 2022 - Housing Census

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

Geographic Coverage

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

Indicators, Variables, Or Resource Contents

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

Schema

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-private-households-by-geographical-location-and-availabili-db18dac9")
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: Source-File_12.xlsx
• —License: CC BY-SA 4.0
• —Retrieved/generated: 2026-08-08T17:09:59Z
• —Hugging Face repo: electricsheepafrica/africa-mauritius-private-households-by-geographical-location-and-availabili-db18dac9

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_private_households_by_geographical_location_and_availabili_db18_2022,
  title        = {Private Households by Geographical Location and Availabili | Africa (MDPA)},
  author       = {MDPA},
  year         = {2022},
  url          = {https://data.govmu.org/dataset/private-households-geographical-location-and-availability-tv-refrigerator-washing-machine},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-private-households-by-geographical-location-and-availabili-db18dac9}}
}

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/private-households-geographical-location-and-availability-tv-refrigerator-washing-machine