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electricsheepafrica/africa-rwanda-season-a-household-screening-8bbcc329

Season A: Household Screening | Africa (Rwanda Data Sharing Platform - NISR) 54,382 rows - 1 Africa country/area - 2023-09-04-2024-02-26 - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 54,382 rows from Rwanda Data Sharing Platform - NISR, covering Season A: Household Screening. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples. What… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-rwanda-season-a-household-screening-8bbcc329.

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Dataset Card

Season A: Household Screening | Africa (Rwanda Data Sharing Platform - NISR)

54,382 rows - 1 Africa country/area - 2023-09-04-2024-02-26 - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 54,382 rows from Rwanda Data Sharing Platform - NISR, covering Season A: Household Screening. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Agriculture datasets help analysts examine production, prices, inputs, land use, food systems, and rural economic activity.

Source-provided context: Summary: This table presents plot-level screening data on land use and crop characteristics during Season A (September 2023 – February 2024) of the 2023/2024 Seasonal Agricultural Survey (SAS) conducted by the National Institute of Statistics of Rwanda. Each record represents a specific agricultural plot and the screening information recorded for it by the farming household during the reference season. The table includes details on land use type, anti-erosion activities and their types, agroforestry practices including tree types, land consolidation status, cropping system, and crop-level characteristics such as crop name, category, proportion and density of plot coverage, number of plants for banana crops, and whether the crop was planted and will be harvested during the season. Geographic Coverage: National coverage across Rwanda, including all provinces and districts, capturing land use and crop characteristics in both rural and peri-urban areas.

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
Rows54,382
Countries/areas1
First period2023-09-04
Last period2024-02-26
Indicators0
Columns57
Source formatCSV

Geographic Coverage

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

AreaRowsFirst yearLast yearName
RWA54,3822023-09-042024-02-26Rwanda

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.0fa0d474-9901-4a35-a7dd-b414b8e02f94:0
country_iso3stringISO3 country or area code.RWA
country_namestringCountry or area name.Rwanda
idint64Row ID1
segment_iddoubleSource column from the original resource.12001.0
s1q1stringProvince nameKigali
s1q2stringDistrict nameGasabo
s1q3stringStratum classificationLarge Scale Farmer/LSF
s1q4doubleIdentification number of the segment99.0
s1q5_2doubleIdentification number of the farmer or large-scale farmer who grown crops in this plot1.0
s1q10stringCategory of the farmer or large-scale farmer (small-scale farmer or large-scale farmer)Individual large farmer
s1q13doubleNumber of grid points sampled in the segment99.0
s2q1doublePlot number1.0
s2q2doubleBloc number in case of large-scale farmer55.0
s2q3doubleNumber of grid points that fall in this plot99.0
s2q4doubleList of grid identifiers that fall within this plot``
s2q5_blocdoubleBloc area in case of large-scale farmer (m2)69335.673
s2q5doublePlot size (m2)69335.67
s2q6stringLand use type of the plot e.g. seasonal crops permanent crops pasturePasture
s2q7stringType of non-agricultural land use on this plot``
s2q7_otherstringSource column from the original resource.``
s2q8stringIndicates whether any anti-erosion activity exists on this plotYes
s2q9_1stringFirst type of anti-erosion activity existing on this plotWater channel
s2q9_2stringSecond type of anti-erosion activity existing on this plot``
s2q9_3stringThird type of anti-erosion activity existing on this plot``
s2q9_ostringOther specified type of anti-erosion activity existing on this plot (open text)``
s2q10stringIndicates whether any agroforestry practice exists on this plotNo
s2q11_1stringFirst type of agroforestry tree planted on this plot``
s2q11_2stringSecond type of agroforestry tree planted on this plot``
s2q11_3stringThird type of agroforestry tree planted on this plot``
s2q11_4stringFourth type of agroforestry tree planted on this plot``
s2q11_ostringOther specified type of agroforestry tree planted on this plot (open text)``
s2q12stringIndicates whether the plot is located in a land consolidation siteNo
s2q13stringCropping system used on this plot e.g. monoculture or intercroppingPure Cropping
s2q14doubleNumber of main crops grown in the plot1.0
s3q1stringName and code of the crop grown on this plotNapia grass for fodder
s3q2_1doubleProportion of the plot area covered by this crop (%)100.0
s3q2_2stringCoded category of crop proportion on this plot91%-100%
s3q3_1doubleDensity of the crop on this plot (%)100.0
s3q3_2stringCoded category of crop density on this plot91%100%
s3q4doubleNumber of banana plants in the plot (applicable for banana plantations only)``
s3q5stringIndicates whether this crop was planted during the current agricultural seasonYes
s3q6stringIndicates whether this crop will be harvested during the current agricultural seasonYes
s3q7stringExpected harvesting period for this crop during the seasonBefore 01/12
cropcategorystringSource column from the original resource.Fodder crops
s3q1_ostringOther specified crop name (open text)``
plot_weightdoubleStatistical plot weight for deriving nationally representative estimates1.0
plot_size_hadoubleSource column from the original resource.6.933567
crop_areadoubleEstimated area of the crop on this plot based on crop proportion and plot size6.933567
source_providerstringPublishing organization.NISR
source_datasetstringSource dataset or package title.Season A: Household Screening
source_resourcestringSource resource title, table name, or file name.sas_2024_seasonA_screening
source_package_idstringSource package identifier.0fa0d474-9901-4a35-a7dd-b414b8e02f94
source_resource_idstringSource resource identifier.0fa0d474-9901-4a35-a7dd-b414b8e02f94
source_urlstringOriginal source URL or download URL.https://api.data.gov.rw/api/v1/datasets/public/0fa0d474-9901-4a35-a7d...
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-season-a-household-screening-8bbcc329")
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

  • —Track production or price movements
  • —Compare regions or commodities
  • —Join with climate and trade data
  • —Check missingness before modeling
  • —Use country_iso3 as the safest geography join key when present

Citation

bibtex
@misc{electric_sheep_africa_africa_rwanda_season_a_household_screening_8bbcc329_2024,
  title        = {Season A: Household Screening | Africa (Rwanda Data Sharing Platform - NISR)},
  author       = {NISR},
  year         = {2024},
  url          = {https://api.data.gov.rw/api/v1/datasets/public/0fa0d474-9901-4a35-a7dd-b414b8e02f94},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-rwanda-season-a-household-screening-8bbcc329}}
}

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/0fa0d474-9901-4a35-a7dd-b414b8e02f94