electricsheepafrica/africa-rwanda-eicv7-vup-classic-public-work-c660cd2c

EICV7 (VUP): Classic public work | Africa (Rwanda Data Sharing Platform - NISR)

925 rows - 1 Africa country/area - 2023-10-16-2024-10-15 - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

TL;DR

This dataset contains 925 rows from Rwanda Data Sharing Platform - NISR, covering EICV7 (VUP): Classic public work. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Education datasets help analysts study access, participation, learning systems, infrastructure, and outcomes across places and periods.

Source-provided context: Summary: This microdata table contains information on participation in classic public work collected in the seventh Integrated Household Living Conditions Survey, known as EICV7 (Enquête Intégrale sur les Conditions de Vie des ménages). A main cross-sectional sample survey, a panel survey and a VUP (Vision Umurenge Programme) sample survey were conducted simultaneously; this microdata table relates to the VUP sample survey. Geographic Coverage: National coverage (Rwanda), including rural and urban households and allowing province- and district-level estimation of key indicators. Time Period: The EICV7 data collection covered a 12 month period (October 2023 to October 2024). In order to represent the seasonality in the income and consumption data, the fieldwork was divided into nine nationally representative cycles. Frequency: The EICV is conducted every three years; prior to EICV4, the survey was conducted every five years, with the first survey (EICV1) conducted in 2000/01.

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: not detected.
• —Time coverage basis: source metadata period.
• —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-rwanda-eicv7-vup-classic-public-work-c660cd2c")
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"] == "RWA"]

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

• —No canonical year/date column was detected in the packaged table; use source metadata and domain context for temporal interpretation.
• —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: Rwanda Data Sharing Platform - NISR
• —Publisher: NISR
• —Portal: https://data.gov.rw/
• —Resource: eicv7_vup_s9d2_classic_public_work
• —License: CC BY 4.0
• —Retrieved/generated: 2026-07-18T11:46:48Z
• —Hugging Face repo: electricsheepafrica/africa-rwanda-eicv7-vup-classic-public-work-c660cd2c

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

• —Compare education indicators by geography
• —Track participation or completion trends
• —Join with population and poverty indicators
• —Check missingness before modeling
• —Use country_iso3 as the safest geography join key when present

Citation

@misc{electric_sheep_africa_africa_rwanda_eicv7_vup_classic_public_work_c660cd2c_2024,
  title        = {EICV7 (VUP): Classic public work | Africa (Rwanda Data Sharing Platform - NISR)},
  author       = {NISR},
  year         = {2024},
  url          = {https://api.data.gov.rw/api/v1/datasets/public/162d90bf-aee6-4cc5-bd51-8b1501ba1c6b},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-rwanda-eicv7-vup-classic-public-work-c660cd2c}}
}

License

Released under CC BY 4.0.

Original data is published by NISR. 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 2026-08-12 by the Electric Sheep Africa README system. Source URL: https://api.data.gov.rw/api/v1/datasets/public/162d90bf-aee6-4cc5-bd51-8b1501ba1c6b