electricsheepafrica/africa-mauritius-residential-amp-partly-residential-buildings-ever-affected-1acc2582

Residential Amp Partly Residential Buildings Ever Affected | Africa (MDPA)

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

TL;DR

This dataset contains 214 rows from MDPA, covering Residential Amp Partly Residential Buildings Ever Affected. 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 Residential & Partly Residential Buildings Ever Affected By Severe Flooding 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-residential-amp-partly-residential-buildings-ever-affected-1acc2582")
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_16.xlsx
• —License: CC BY-SA 4.0
• —Retrieved/generated: 2026-08-08T17:09:39Z
• —Hugging Face repo: electricsheepafrica/africa-mauritius-residential-amp-partly-residential-buildings-ever-affected-1acc2582

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_residential_amp_partly_residential_buildings_ever_affected_1acc_2022,
  title        = {Residential Amp Partly Residential Buildings Ever Affected | Africa (MDPA)},
  author       = {MDPA},
  year         = {2022},
  url          = {https://data.govmu.org/dataset/residential-partly-residential-buildings-ever-affected-severe-flooding-mauritius-rodrigues},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-residential-amp-partly-residential-buildings-ever-affected-1acc2582}}
}

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/residential-partly-residential-buildings-ever-affected-severe-flooding-mauritius-rodrigues