electricsheepafrica/africa-south-africa-capital-acquisition-2015-0d92d74f

Capital Acquisition 2015 | Africa (National Treasury, South Africa)

2,809,998 rows - 1 Africa country/area - 2015-2023 - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

TL;DR

This dataset contains 2,809,998 rows from National Treasury, South Africa, covering Capital Acquisition 2015. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Economic datasets help analysts examine production, prices, public finance, trade flows, market conditions, and macroeconomic change.

Source-provided context: capitalfactsv1 bulk CSV group 2015.

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.
• —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-capital-acquisition-2015-0d92d74f")
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 "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: National Treasury, South Africa
• —Publisher: National Treasury, South Africa
• —Portal: https://municipaldata.treasury.gov.za
• —Resource: Capital Acquisition - 2015
• —License: other-open
• —Retrieved/generated: 2026-07-22T19:32:04Z
• —Hugging Face repo: electricsheepafrica/africa-south-africa-capital-acquisition-2015-0d92d74f

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 time-series dashboards
• —Compare economic indicators
• —Join with population or sector data
• —Check missingness before modeling
• —Use country_iso3 as the safest geography join key when present

Citation

@misc{electric_sheep_africa_africa_south_africa_capital_acquisition_2015_0d92d74f_2023,
  title        = {Capital Acquisition 2015 | Africa (National Treasury, South Africa)},
  author       = {National Treasury, South Africa},
  year         = {2023},
  url          = {https://municipaldata.treasury.gov.za/docs#cube-capital},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-south-africa-capital-acquisition-2015-0d92d74f}}
}

License

Released under other-open.

Original data is published by National Treasury, 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://municipaldata.treasury.gov.za/docs#cube-capital