electricsheepafrica/africa-rwanda-eicv5-person-8f860ec1

EICV5: Person | Africa (Rwanda Data Sharing Platform - NISR)

64,314 rows - 1 Africa country/area - 2016-10-13-2017-10-22 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 64,314 rows from Rwanda Data Sharing Platform - NISR, covering EICV5: Person. 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 personal characteristics 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

Dimension	Value
Rows	64,314
Countries/areas	1
First period	2016-10-13
Last period	2017-10-22
Indicators	0
Columns	127
Source format	CSV

Geographic Coverage

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

Area	Rows	First year	Last year	Name
RWA	64,314	2016-10-13	2017-10-22	Rwanda

Indicators, Variables, Or Resource Contents

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

Schema

Column	Type	Description	Example
source_record_id	string	Stable row identifier assigned during Electric Sheep Africa engineering.	9f5587d3-01d4-4c70-86ab-62971c9ad67c:0
country_iso3	string	ISO3 country or area code.	RWA
country_name	string	Country or area name.	Rwanda
id	int64	Row ID	1
hhid	double	Household ID	200001.0
pid	double	Person ID Number	1.0
clust	double	Cluster	10001.0
province	string	Province	Kigali City
district	string	District Name	Nyarugenge
ur	string	Urban/Rural	Urban
region	string	Region	Kigali
weight	double	Household weight	159.24864
poverty	string	Welfare categories	Non Poor
quintile	string	Quintile	Q5
s1q1	string	Sex	Male
s1q2	string	Relation with the head of household	Household head _HH_
s1q3y	int64	Age Years	33
s1q3m	double	Age Months	``
s1q4	string	Marital status	Married monogamously with legal certificate
s1q5	string	Place of birth	Muhanga
s1q6	string	Nationality	Rwandese
s1q7	bool	Absent from household in the last 12 months	True
s1q8	string	Length of absence in the last 12 months	Less than one month
s1q9	string	Main reason of absence from household in the last 12 months	Other, specify
s1q10	string	Resident of another household in the last 12 months	Yes
s1q11	string	Resident during enumeration period	Yes
s1q12	string	Main reason for absence during enumeration	``
s1q13	string	Major disability	Without Disability
s1q14	string	Father still alive	``
s1q15	string	Mother still alive	``
s1q16	bool	Household membership	True
s2q1	double	ID number of respondent	1.0
s2q2	bool	Always lived in the district	False
s2q3y	string	Duration since last move years	2.0
s2q3m	double	Duration since last move months	6.0
s2q4	string	District or foreign country lived prior to current place	Gasabo
s2q5	string	Kind of place lived prior to current place	Capital
s2q6	string	Duration lived in the previuos location years	12.0
s2q7	string	Primary reason for moving	Employment
s2q8	string	Move alone or with other members of your household	Alone
s3q1	double	ID number of respondent	1.0
s3q2	string	Main health insurance carry	Mutual insurance
s3q3	bool	Suffer from any health problem in the last 4 weeks	False
s3q4	string	Medical consultation in the over last 4 weeks	No
s3q5	string	Main reason for medical consultation	``
s3q6	string	Difficulty in seeing	No, no difficulty
s3q7	string	Difficulty hearing	No, no difficulty
s3q8	string	Difficulty in moving	No, no difficulty
s3q9	string	Difficulty in remembering or concentrating	No, no difficulty
s3q10	string	Difficulty in speaking/ talking understanding and communicating with others	No, no difficulty
s3q11	string	Difficulty in self-care such as: washing all over or dressing	No, no difficulty
s4aq1	string	Ever been to school	True
s4aq2	string	Highest class succesfully attained in school	Primary 4
s4aq3	string	Highest diploma/certificate/ degree you have ever obtained	``
s4aq4b	double	ISCED	``
s4aq5	string	Last institution of higher learning you attended	``
s4aq6a	string	Class attended in 2015	Not in class
s4aq6b	string	Class attended in 2016	Not in class
s4aq6c	string	Cause of repeat the 2015 class in 2016	``
s4aq7	string	Attended school in the last 12 months	False
s4aq8	string	Type of school	``
s4aq9	string	Problems with school	``
s4aq10	string	Separate toilet facilities at school	``
s4aq11a	string	Registration and school fees	``
s4aq11b	string	Parent contributions	``
s4aq11c	string	School uniforms	``
s4aq11d	double	Books and school supplies	``
s4aq11e	string	Transport to and from school	``
s4aq11f	string	Room and board or board only	``
s4aq11g	string	Other schooling expenditures	``
s4aq11h	double	Total education expenses non-distributed option	``
s4aq12	string	Responsible for payment of majority of school related expenditures	``
s4aq13	string	Miss any day at school last 7 days?	``
s4aq14	double	Many school days not attend last week	``
s4aq15	string	Primary cause of not attending school	``
s4aq16	double	Year did leave school	1995.0
s4aq17	string	Reason for leaving school	Cost
s4bq1	string	Did you attended any vocational training	I did not attend any
s4bq2	string	Expense of this training including fees, expenses and accommodation	``
s4bq3	string	Have you attended a literacy course?	False
s4bq4	string	Able to read a simple note	``
s4bq5	string	Able to write a simple note	``
s4bq6	string	Able to do a written calculation	``
s4bq7	string	Have a mobile telephone	``
s4bq7a	string	Phone has radio and/or internet function?	``
s4bq8a	string	Own a computer	``
s4bq8b	string	Computer literacy	``
s4bq9a	string	Copying or moving a file or a folder	``
s4bq9b	string	Using copy and paste tools to duplicate or move information	``
s4bq9c	string	Sending e-mails with attached files	``
s4bq9d	string	Using basic arithmetic formulae in a spreadsheet	``
s4bq9e	string	Connecting and installing new devices	``
s4bq9f	string	Finding, downloading, installing and configuring software	``
s4bq9g	string	Creating electronic presentations with presentation software	``
s4bq9h	string	Transferring files between a computer and other devices	``
s4bq9i	string	Writing a computer program using a specialized programming language	``
s4bq9j	string	Using computers, Smart Phones, Tablets.. to retrieve, access, store, produce, pr	``
s4bq9k	string	Use of the internet in the last three months for finding information about goods	``
s4bq9l	string	Use of internet in the last three months for reading or downloading online news/	``
s4bq9m	string	Use of the internet in the last three months for obtaining information from webs	``
s4bq9n	string	Use of the internet in the last three months for seeking health information	``
s6aq1	double	ID number of respondent	``
s6aq2	string	Worked on own farm no payment	``
s6aq3	string	Carry out any agricultural activity	``
s6aq4	string	Worked for salary or wages in non-farm sector in the last 12 months	``
s6aq5	string	Run a non-farm business for cash or profit in the last 12 months	``
s6aq6	string	Worked in a non-fam business in the last 12 months	``
s6aq7a	double	Many jobs over past 12 months	``
s6aq7b	double	Many jobs over past 7 days	``
s6aq8	double	Many months has been occupied during the last 12 months	``
s6aq9	string	Main reason not working over the last	``
s6eq1	string	Over last 7 days gather, collect or purchase main fuel for cooking	``
s6eq2	double	Total minutes for Gathering, collecting or purchasing the main fuel	``
s6eq3	string	Main fuel and the main cook stove over the last 7 days	``
s6eq4	double	Total minutes for main fuel and main cooking stove 7 days period	``
s6eq5	string	Cooking on the main cook stove over the last 7 days	``
s6eq6	double	Total minutes spent Cooking on the main cook stove	``
s0q18m	int64	Month of interview	``
s0q18y	int64	Year of interview	``
source_provider	string	Publishing organization.	``
source_dataset	string	Source dataset or package title.	``
source_resource	string	Source resource title, table name, or file name.	``
source_package_id	string	Source package identifier.	``
source_resource_id	string	Source resource identifier.	``
source_url	string	Original source URL or download URL.	``
license_id	string	Source license identifier.	``
retrieved_at	string	UTC source retrieval timestamp from the Electric Sheep Africa pipeline.	``

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-rwanda-eicv5-person-8f860ec1")
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: eicv5_s1_s2_s3_s4_s6a_s6e_person
• License: CC BY 4.0
• Retrieved/generated: 2026-07-18T10:58:15Z
• Hugging Face repo: electricsheepafrica/africa-rwanda-eicv5-person-8f860ec1

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

@misc{electric_sheep_africa_africa_rwanda_eicv5_person_8f860ec1_2017,
  title        = {EICV5: Person | Africa (Rwanda Data Sharing Platform - NISR)},
  author       = {NISR},
  year         = {2017},
  url          = {https://api.data.gov.rw/api/v1/datasets/public/9f5587d3-01d4-4c70-86ab-62971c9ad67c},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-rwanda-eicv5-person-8f860ec1}}
}

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.

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Provenance: README standardized 2026-08-12 by the Electric Sheep Africa README system. Source URL: https://api.data.gov.rw/api/v1/datasets/public/9f5587d3-01d4-4c70-86ab-62971c9ad67c