electricsheepafrica/africa-rwanda-agricultural-workforce-by-education-level-0bfaa89f

Agricultural Workforce by Education Level | Africa (Rwanda Data Sharing Platform - NISR)

28 rows - 1 Africa country/area - 2019-2025 - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

TL;DR

This dataset contains 28 rows from Rwanda Data Sharing Platform - NISR, covering Agricultural Workforce by Education Level. 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: Summary: This table is part of the Rwanda Labour Force Survey (LFS) 2025 aggregated schema, which compiles quarterly labour market data from 2019 to 2025 to provide a continuous and longitudinal view of workforce dynamics. It presents the share of workers employed in the agricultural sector within the total workforce, disaggregated by education level. The table shows the percentage distribution of agricultural workers across different levels of educational attainment, enabling analysis of how education shapes participation in agriculture over time. Geographic Coverage: National coverage across Rwanda, with potential breakdowns by urban and rural areas. Time Period: Quarterly data from 2019 Q1 to 2025 Q4. Frequency: Quarterly Population/Units: Individuals aged 16 years and above in the workforce, specifically those employed in agriculture. Key Variables: Education level, number of agricultural workers, share of agricultural workers in total workforce (percentage).

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-rwanda-agricultural-workforce-by-education-level-0bfaa89f")
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

• —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: Rwanda Data Sharing Platform - NISR
• —Publisher: NISR
• —Portal: https://data.gov.rw/
• —Resource: agri_workers_by_education
• —License: CC BY 4.0
• —Retrieved/generated: 2026-07-18T13:09:52Z
• —Hugging Face repo: electricsheepafrica/africa-rwanda-agricultural-workforce-by-education-level-0bfaa89f

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_rwanda_agricultural_workforce_by_education_level_0bfaa89f_2025,
  title        = {Agricultural Workforce by Education Level | Africa (Rwanda Data Sharing Platform - NISR)},
  author       = {NISR},
  year         = {2025},
  url          = {https://api.data.gov.rw/api/v1/datasets/public/12fa4854-e852-4963-9d2f-da7907c3dbaa},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-rwanda-agricultural-workforce-by-education-level-0bfaa89f}}
}

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/12fa4854-e852-4963-9d2f-da7907c3dbaa