electricsheepafrica/africa-mauritius-pension-for-invalid-orphan-retirement-severelyhandicapped-aec4b0e3

Pension for Invalid Orphan Retirement Severelyhandicapped | Africa (MDPA)

75 rows - 1 Africa country/area - 1990-2014 - 3 indicators - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

TL;DR

This dataset contains 75 rows from MDPA, covering Pension for Invalid Orphan Retirement Severelyhandicapped. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Official statistics datasets help analysts inspect public data as published by governments, national statistical systems, and regional data portals.

Source-provided context: Pension for Invalid, Orphan, Retirement, Severely Handicapped, Widow 1990 to 2014

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

• —pension-for-invalid-orphan-retirement-severelyhandicapped-widow-male-15266693 - Pension for Invalid-Orphan-Retirement-SeverelyHandicapped-Widow - male(sourceunitsunspecified)
• —pension-for-invalid-orphan-retirement-severelyhandicapped-widow-female-88e6176d - Pension for Invalid-Orphan-Retirement-SeverelyHandicapped-Widow - female(sourceunitsunspecified)
• —pension-for-invalid-orphan-retirement-severelyhandicapped-widow-both-sex-b3cb1cfb - Pension for Invalid-Orphan-Retirement-SeverelyHandicapped-Widow - both sexes(sourceunitsunspecified)

Schema

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-pension-for-invalid-orphan-retirement-severelyhandicapped-aec4b0e3")
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: Basic-Widow-Pension.csv
• —License: CC BY-SA 4.0
• —Retrieved/generated: 2026-08-08T16:33:40Z
• —Hugging Face repo: electricsheepafrica/africa-mauritius-pension-for-invalid-orphan-retirement-severelyhandicapped-aec4b0e3

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

• —Profile the distribution of values
• —Compare categories or geographies
• —Join with complementary public datasets
• —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_pension_for_invalid_orphan_retirement_severelyhandicapped_aec4b_2014,
  title        = {Pension for Invalid Orphan Retirement Severelyhandicapped | Africa (MDPA)},
  author       = {MDPA},
  year         = {2014},
  url          = {https://data.govmu.org/dataset/pension-invalid-orphan-retirement-severelyhandicapped-widow},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-pension-for-invalid-orphan-retirement-severelyhandicapped-aec4b0e3}}
}

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/pension-invalid-orphan-retirement-severelyhandicapped-widow