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electricsheepafrica/africa-rwanda-table-2-2-percentage-of-international-migrants-in-the-last-f795c284

Table 2.2: Percentage of international migrants in the last five years, by previous country, consumption quintile, sex and place of residence in relation to area of residence and province | Africa (Rwanda Data Sharing Platform - NISR) 15 rows - 1 Africa country/area - 2023-10-16-2024-10-15 - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 15 rows from Rwanda Data Sharing Platform - NISR, covering Table 2.2: Percentage of… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-rwanda-table-2-2-percentage-of-international-migrants-in-the-last-f795c284.

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Table 2.2: Percentage of international migrants in the last five years, by previous country, consumption quintile, sex and place of residence in relation to area of residence and province | Africa (Rwanda Data Sharing Platform - NISR)

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

rows countries period indicators license

TL;DR

This dataset contains 15 rows from Rwanda Data Sharing Platform - NISR, covering Table 2.2: Percentage of international migrants in the last five years, by previous country, consumption quintile, sex and place of residence in relation to area of residence and province. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Demographic datasets help analysts understand population structure, household conditions, migration, gender, age, and settlement patterns.

Source-provided context: Summary: This aggregated data table contains data on the percentage of international migrants in the last five years, by previous country, consumption quintile, sex and place of residence in relation to area of residence and province. This data was collected in the seventh Integrated Household Living Conditions Survey, known as EICV7 (Enquête Intégrale sur les Conditions de Vie des ménages). 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

DimensionValue
Rows15
Countries/areas1
First period2023-10-16
Last period2024-10-15
Indicators0
Columns24
Source formatCSV

Geographic Coverage

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

AreaRowsFirst yearLast yearName
RWA152023-10-162024-10-15Rwanda

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.bb29490d-a749-4bdf-98bc-20ef51dbba8b:0
country_iso3stringISO3 country or area code.RWA
country_namestringCountry or area name.Rwanda
idint64Source column from the original resource.1
area_of_residence_province_sex_consumption_quintilestringSource column from the original resource.Rwanda
international_migrants_in_the_last_5_yearsdoubleSource column from the original resource.0.598865127
total_population_000sdoubleSource column from the original resource.13549.45694
previous_country_burundidoubleSource column from the original resource.14.70635595
previous_country_dr_congodoubleSource column from the original resource.33.43916441
previous_country_ugandadoubleSource column from the original resource.23.71377205
previous_country_tanzaniadoubleSource column from the original resource.26.02940304
previous_country_kenyadoubleSource column from the original resource.0.542454115
previous_country_other_africandoubleSource column from the original resource.0.530772972
previous_country_rest_of_the_worlddoubleSource column from the original resource.1.038077468
totalint64Source column from the original resource.100
total_international_migrants_population_000sdoubleSource column from the original resource.81.14297252
source_providerstringPublishing organization.NISR
source_datasetstringSource dataset or package title.Table 2.2: Percentage of international migrants in the last five year...
source_resourcestringSource resource title, table name, or file name.eicv7_main_indicators_table2.2
source_package_idstringSource package identifier.bb29490d-a749-4bdf-98bc-20ef51dbba8b
source_resource_idstringSource resource identifier.bb29490d-a749-4bdf-98bc-20ef51dbba8b
source_urlstringOriginal source URL or download URL.https://api.data.gov.rw/api/v1/datasets/public/bb29490d-a749-4bdf-98b...
license_idstringSource license identifier.cc-by-4.0
retrieved_atstringUTC source retrieval timestamp from the Electric Sheep Africa pipeline.2026-07-18T11:48:51Z

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-rwanda-table-2-2-percentage-of-international-migrants-in-the-last-f795c284")
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

  • —Build demographic profiles
  • —Normalize indicators per capita
  • —Join with service-delivery datasets
  • —Check missingness before modeling
  • —Use country_iso3 as the safest geography join key when present

Citation

bibtex
@misc{electric_sheep_africa_africa_rwanda_table_2_2_percentage_of_international_migrants_in_the_last_f795c28_2024,
  title        = {Table 2.2: Percentage of international migrants in the last five years, by previous country, consumption quintile, sex and place of residence in relation to area of residence and province | Africa (Rwanda Data Sharing Platform - NISR)},
  author       = {NISR},
  year         = {2024},
  url          = {https://api.data.gov.rw/api/v1/datasets/public/bb29490d-a749-4bdf-98bc-20ef51dbba8b},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-rwanda-table-2-2-percentage-of-international-migrants-in-the-last-f795c284}}
}

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/bb29490d-a749-4bdf-98bc-20ef51dbba8b