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electricsheepafrica/africa-rwanda-eicv5-vup-ubudehe-and-rssp-schemes-3-d74577e3

EICV5: VUP, Ubudehe, and RSSP Schemes (3) | Africa (Rwanda Data Sharing Platform - NISR) 137 rows - 1 Africa country/area - 2016-10-13-2017-10-22 - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 137 rows from Rwanda Data Sharing Platform - NISR, covering EICV5: VUP, Ubudehe, and RSSP Schemes (3). It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-rwanda-eicv5-vup-ubudehe-and-rssp-schemes-3-d74577e3.

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Dataset Card

EICV5: VUP, Ubudehe, and RSSP Schemes (3) | Africa (Rwanda Data Sharing Platform - NISR)

137 rows - 1 Africa country/area - 2016-10-13-2017-10-22 - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 137 rows from Rwanda Data Sharing Platform - NISR, covering EICV5: VUP, Ubudehe, and RSSP Schemes (3). 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: Summary: This microdata table contains information on VUP, Ubudehe, and RSSP schemes collected in the fifth Integrated Household Living Conditions Survey, known as EICV5 (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 cross-sectional sample survey. Geographic Coverage: National coverage (Rwanda), including rural and urban households and allowing province- and district-level estimation of key indicators. Data are disaggregated up to the district level. Time Period: The EICV5 survey was conducted over a 12-month cycle from October 2016 to October 2017. Data collection was divided into 10 cycles in order to represent seasonality in the income and consumption data.

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

DimensionValue
Rows137
Countries/areas1
First period2016-10-13
Last period2017-10-22
Indicators0
Columns54
Source formatCSV

Geographic Coverage

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

AreaRowsFirst yearLast yearName
RWA1372016-10-132017-10-22Rwanda

Indicators, Variables, Or Resource Contents

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

Schema

ColumnTypeDescriptionExample
source_record_idstringStable row identifier assigned during Electric Sheep Africa engineering.ea35fe3b-8e2e-4408-9e14-2759255d2f34:0
country_iso3stringISO3 country or area code.RWA
country_namestringCountry or area name.Rwanda
idint64Row ID1
hhiddoubleHousehold ID201841.0
clustdoubleCluster10199.0
provincestringProvinceSouthern Province
districtstringDistrict NameNyanza
urstringUrban/RuralRural
regionstringRegionSouthern rural
weightdoubleHousehold weight155.93063
povertystringWelfare categoriesNon Poor
quintilestringQuintileQ3
s9cq1stringHousehold category in Ubudehe VUP_Category 3
s9cq2stringHousehold ever received any income /Loan from VUPCurrently VUP beneficiary
s9cq4astringSpecify the VUP programmes that your HH benefitted in year 2008Financial services only
s9cq4bstringSpecify the VUP programmes that your HH benefitted in year 2009Not applicable/No Benefit
s9cq4cstringSpecify the VUP programmes that your HH benefitted in year 2010Not applicable/No Benefit
s9cq4dstringSpecify the VUP programmes that your HH benefitted in year 2011Not applicable/No Benefit
s9cq4estringSpecify the VUP programmes that your HH benefitted in year 2012Not applicable/No Benefit
s9cq4fstringSpecify the VUP programmes that your HH benefitted in year 2013Not applicable/No Benefit
s9cq4gstringSpecify the VUP programmes that your HH benefitted in year 2014Financial services only
s9cq4hstringSpecify the VUP programmes that your HH benefitted in year 2015Financial services only
s9cq4istringSpecify the VUP programmes that your HH benefitted in year 2016Financial services only
s9cq4jstringSpecify the VUP programmes that your HH benefitted in year 2017Financial services only
s9c4q00int64ID of the person receiving FS loan1
s9c4q01int64Loan Id1
s9c4q30aint64Loan received_Month11
s9c4q30bint64Loan received_year2016
s9c4q31stringType of loan financial servicesGroup _informal_
s9c4q32doubleNumber of people included in the Financial Services loan application23.0
s9c4q33doublepeople involved in the group /cooperative23.0
s9c4q34int64The total amount of the loan received117000
s9c4q35int64Mount already been repaided by you to the SACCO/ account117000
s9c4q36adoubleLast payment_month2.0
s9c4q36bdoubleLast payment_year2017.0
s9c4q37doubleAmount of loan used independently of the group/ cooperative117000.0
s9c4q38astringUse of FS loan for household?s basic expenses_firstEducation
s9c4q38bstringUse of Fs loan for household's basic espenses_secondBuy animals/ Invest in farm
s9c4q39stringMain project activity that originally planned to do using the loanInvestment in Farming
s9c4q40int64Loan amount used to cover other expenses117000
s9c4q41stringAre you implementing the same project applied for or you changed?Yes, same project
s9c4q42stringMain current project activity``
s9c4q43doublePeople involved in the project other than group members0.0
s9c4q44stringIs the project profitable?True
s9c4q99stringMore FS loan receivedFalse
source_providerstringPublishing organization.NISR
source_datasetstringSource dataset or package title.EICV5: VUP, Ubudehe, and RSSP Schemes (3)
source_resourcestringSource resource title, table name, or file name.eicv5_s9c4_vup_ubudehe_and_rssp_schemes
source_package_idstringSource package identifier.ea35fe3b-8e2e-4408-9e14-2759255d2f34
source_resource_idstringSource resource identifier.ea35fe3b-8e2e-4408-9e14-2759255d2f34
source_urlstringOriginal source URL or download URL.https://api.data.gov.rw/api/v1/datasets/public/ea35fe3b-8e2e-4408-9e1...
license_idstringSource license identifier.cc-by-4.0
retrieved_atstringUTC source retrieval timestamp from the Electric Sheep Africa pipeline.2026-07-18T10:37:04Z

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-rwanda-eicv5-vup-ubudehe-and-rssp-schemes-3-d74577e3")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

python
print(df.info())
print(df.head())

Filter By Geography

python
if "country_iso3" in df.columns:
    sample = df[df["country_iso3"] == "RWA"]

Time-Series Pattern

python
if "value" in df.columns and "year" in df.columns:
    trend = df.sort_values("year")

Pivot For Analysis

python
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

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
  • —Check missingness before modeling
  • —Use country_iso3 as the safest geography join key when present

Citation

bibtex
@misc{electric_sheep_africa_africa_rwanda_eicv5_vup_ubudehe_and_rssp_schemes_3_d74577e3_2017,
  title        = {EICV5: VUP, Ubudehe, and RSSP Schemes (3) | Africa (Rwanda Data Sharing Platform - NISR)},
  author       = {NISR},
  year         = {2017},
  url          = {https://api.data.gov.rw/api/v1/datasets/public/ea35fe3b-8e2e-4408-9e14-2759255d2f34},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-rwanda-eicv5-vup-ubudehe-and-rssp-schemes-3-d74577e3}}
}

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/ea35fe3b-8e2e-4408-9e14-2759255d2f34