electricsheepafrica/africa-south-africa-stats-sa-census-district-boundary-properties-56c30d76

Stats Sa Census District Boundary Properties | Africa (Statistics South Africa)

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

TL;DR

This dataset contains 8 rows from Statistics South Africa, covering Stats Sa Census District Boundary Properties. 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: Feature properties and geometry JSON from the public Census dissemination map asset.

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: source_period_start_year.
• —Time coverage basis: sourceperiodstart_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-south-africa-stats-sa-census-district-boundary-properties-56c30d76")
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"] == "ZAF"]

Time-Series Pattern

if "value" in df.columns and "source_period_start_year" in df.columns:
    trend = df.sort_values("source_period_start_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: source_period_start_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: Statistics South Africa
• —Publisher: Statistics South Africa
• —Portal: https://census.statssa.gov.za
• —Resource: Stats SA Census district boundary properties
• —License: other-open
• —Retrieved/generated: 2026-07-22T20:38:27Z
• —Hugging Face repo: electricsheepafrica/africa-south-africa-stats-sa-census-district-boundary-properties-56c30d76

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
• —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_south_africa_stats_sa_census_district_boundary_properties_56c30d76_2026,
  title        = {Stats Sa Census District Boundary Properties | Africa (Statistics South Africa)},
  author       = {Statistics South Africa},
  year         = {2026},
  url          = {https://census.statssa.gov.za},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-south-africa-stats-sa-census-district-boundary-properties-56c30d76}}
}

License

Released under other-open.

Original data is published by Statistics South Africa. 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://census.statssa.gov.za