---
license: other
language:
- en
task_categories:
- tabular-classification
- tabular-regression
multilinguality: multilingual
size_categories:
- 100K<n<1M
tags:
- "tabular"
- "africa"
- "open-data"
- "official-statistics"
- "south-africa"
- "national-treasury-south-africa"
- "economics"
- "capital-acquisition-v2"
- "capital-v2"
- "capital-facts-v2"
- "2026"
- "15115064-bytes"
- "f865485461a7905f4199573bc39b9470dd149b71"
- "municipal-money"
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-00000-of-00001.parquet
pretty_name: "Capital Acquisition V2 2026 | Africa (National Treasury, South Africa)"
---

# Capital Acquisition V2 2026 | Africa (National Treasury, South Africa)

**117,717 rows** - **1 Africa country/area** - **2026** - **source table** - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*

![rows](https://img.shields.io/badge/rows-117717-blue)
![countries](https://img.shields.io/badge/countries-1-green)
![period](https://img.shields.io/badge/period-2026-orange)
![indicators](https://img.shields.io/badge/indicators-0-purple)
![license](https://img.shields.io/badge/license-other-lightgrey)

## TL;DR

This dataset contains **117,717 rows** from **National Treasury, South Africa**, covering **Capital Acquisition V2 2026**. 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: capital_facts_v2 bulk CSV group 2026.

## 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:** `year`.
- **Time coverage basis:** year.
- **Recommended join keys:** `country_iso3` where available plus source-specific keys.

## Coverage

| Dimension | Value |
|---|---:|
| Rows | 117,717 |
| Countries/areas | 1 |
| First period | 2026 |
| Last period | 2026 |
| Indicators | 0 |
| Columns | 29 |
| Source format | CSV |

## Geographic Coverage

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

| Area | Rows | First year | Last year | Name |
|------|-----:|-----------:|----------:|------|
| `ZAF` | 117,717 | 2026 | 2026 | `South Africa` |

## 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` | `int64` | Stable row identifier assigned during Electric Sheep Africa engineering. | `0` |
| `country_iso3` | `dictionary<values=string, indices=int8, ordered=0>` | ISO3 country or area code. | `ZAF` |
| `country_name` | `dictionary<values=string, indices=int8, ordered=0>` | Country or area name. | `South Africa` |
| `year` | `int64` | Observation year. | `2026` |
| `demarcation_code` | `string` | Source column from the original resource. | `WC015` |
| `name` | `string` | Source column from the original resource. | `Swartland` |
| `code` | `int64` | Source column from the original resource. | `1110` |
| `label` | `string` | Source column from the original resource. | `Mayor and Council` |
| `code_1` | `int64` | Source column from the original resource. | `920` |
| `label_1` | `string` | Source column from the original resource. | `Public Open Space` |
| `code_2` | `string` | Source column from the original resource. | `DEPRECIATION` |
| `label_2` | `string` | Source column from the original resource. | `Deprecation` |
| `year_end` | `int64` | Source column from the original resource. | `2026` |
| `length` | `string` | Source column from the original resource. | `year` |
| `period` | `int64` | Source column from the original resource. | `2026` |
| `code_3` | `string` | Source column from the original resource. | `ADJB` |
| `label_3` | `string` | Source column from the original resource. | `Adjusted Budget` |
| `amount` | `double` | Source column from the original resource. | `10191.0` |
| `source_period_start_year` | `int64` | Start year inferred from source metadata. | `2026` |
| `source_period_end_year` | `int64` | End year inferred from source metadata. | `2026` |
| `source_period_label` | `dictionary<values=string, indices=int8, ordered=0>` | Source column from the original resource. | `2026` |
| `source_provider` | `dictionary<values=string, indices=int8, ordered=0>` | Publishing organization. | `National Treasury, South Africa` |
| `source_dataset` | `dictionary<values=string, indices=int8, ordered=0>` | Source dataset or package title. | `Capital Acquisition (v2) - 2026` |
| `source_resource` | `dictionary<values=string, indices=int8, ordered=0>` | Source resource title, table name, or file name. | `Capital Acquisition (v2) - 2026` |
| `source_package_id` | `dictionary<values=string, indices=int8, ordered=0>` | Source package identifier. | `capital-acquisition-v2-2026-municipal-money-a192ceb0692c8c` |
| `source_resource_id` | `dictionary<values=string, indices=int8, ordered=0>` | Source resource identifier. | `municipal-money-a192ceb0692c8c` |
| `source_url` | `dictionary<values=string, indices=int8, ordered=0>` | Original source URL or download URL. | `https://munimoney-media.s3.eu-west-1.amazonaws.com/munimoney-media/me...` |
| `license_id` | `dictionary<values=string, indices=int8, ordered=0>` | Source license identifier. | `other-open` |
| `retrieved_at` | `dictionary<values=string, indices=int8, ordered=0>` | UTC source retrieval timestamp from the Electric Sheep Africa pipeline. | `2026-07-22T19:25:13Z` |

## Usage

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-south-africa-capital-acquisition-v2-2026-d9a49da8")
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"] == "ZAF"]
```

### 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

- Canonical time field: `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:** [National Treasury, South Africa](https://municipaldata.treasury.gov.za/docs#cube-capital_v2)
- **Publisher:** National Treasury, South Africa
- **Portal:** [https://municipaldata.treasury.gov.za](https://municipaldata.treasury.gov.za)
- **Resource:** [Capital Acquisition (v2) - 2026](https://munimoney-media.s3.eu-west-1.amazonaws.com/munimoney-media/media/bulk_downloads/capital_facts_v2/capital_facts_v2_2026__2026-07-17_15-28-56.csv)
- **License:** other-open
- **Retrieved/generated:** `2026-07-22T19:26:31Z`
- **Hugging Face repo:** [electricsheepafrica/africa-south-africa-capital-acquisition-v2-2026-d9a49da8](https://huggingface.co/datasets/electricsheepafrica/africa-south-africa-capital-acquisition-v2-2026-d9a49da8)

## 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
- 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

```bibtex
@misc{electric_sheep_africa_africa_south_africa_capital_acquisition_v2_2026_d9a49da8_2026,
  title        = {Capital Acquisition V2 2026 | Africa (National Treasury, South Africa)},
  author       = {National Treasury, South Africa},
  year         = {2026},
  url          = {https://municipaldata.treasury.gov.za/docs#cube-capital_v2},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-south-africa-capital-acquisition-v2-2026-d9a49da8}}
}
```

## 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_v2