electricsheepafrica/africa-south-africa-aged-creditor-analysis-v2-2026-48af2506

Aged Creditor Analysis (v2) - 2026 | Africa (South Africa official open data)

148,544 rows - 1 Africa country - 2026 - Repackaged by Electric Sheep Africa

TL;DR

This dataset packages one official CSV resource from South Africa as ML-ready Parquet. The source file is the provenance boundary; all usable indicators or tabular columns from the resource stay together in this repo.

About the source

• Source: Aged Creditor Analysis (v2) - 2026
• Publisher: National Treasury, South Africa
• Resource: Aged Creditor Analysis (v2) - 2026
• Format: CSV
• License: Other open license
• Packaging mode: tabular_resource

Geographic coverage

1 Africa country:

Country	Rows	First year	Last year	Name
ZAF	148,544	2026	2026	South Africa

Indicators or Resource Contents

• This source file is packaged as a normalized tabular resource.

Schema

Column	Type	Description	Example
source_record_id	Int64	Stable row identifier for tabular resources.	0
country_iso3	category	ISO3 country code.	ZAF
country_name	category	Country name.	South Africa
year	Int64	Observation year.	2026
demarcation_code	string	Source column.	BUF
name	string	Source column.	Buffalo City
code	int64	Source column.	100
label	string	Source column.	Bulk Electricity
position_in_return_form	int64	Source column.	1
return_form_structure	string	Source column.	line_item
composition	string	Source column.	``
year_end	int64	Source column.	2020
length	string	Source column.	year
period	int64	Source column.	2020
code_1	string	Source column.	AUDA
label_1	string	Source column.	Audited Actual
over_1_year	int64	Source column.	0
d_181_days_1_year	int64	Source column.	0
d_91_120_days	int64	Source column.	0
d_121_150_days	int64	Source column.	0
d_151_180_days	int64	Source column.	0
d_30_days_and_below	int64	Source column.	199342645
d_31_60_days	int64	Source column.	0
d_61_90_days	int64	Source column.	0
total	int64	Source column.	199342645
source_period_start_year	Int64	First year inferred from source resource metadata.	2026
source_period_end_year	Int64	Last year inferred from source resource metadata.	2026
source_period_label	category	Human-readable period inferred from source resource metadata.	2026
source_provider	category	Publishing organization.	National Treasury, South Africa
source_dataset	category	Source package title.	Aged Creditor Analysis (v2) - 2026
source_resource	category	Source resource title.	Aged Creditor Analysis (v2) - 2026
source_package_id	category	CKAN package UUID.	aged-creditor-analysis-v2-2026-municipal-money-008399bf433418
source_resource_id	category	CKAN resource UUID.	municipal-money-008399bf433418
source_url	category	Original source resource URL.	https://munimoney-media.s3.eu-west-1.amazonaws.com/munimoney-media/media
license_id	category	Source license identifier.	other-open
retrieved_at	category	UTC retrieval timestamp.	2026-07-22T19:07:15Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-south-africa-aged-creditor-analysis-v2-2026-48af2506")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

sample_country = df[df["country_iso3"] == "ZAF"]

Work with indicators

if "indicator_id" in df.columns:
    print(df["indicator_id"].value_counts().head())
    sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns])

Citation

@misc{electric_sheep_africa_africa_south_africa_aged_creditor_analysis_v2_2026_48af2506_2026,
  title        = {Aged Creditor Analysis (v2) - 2026 | Africa (South Africa official open data)},
  author       = {National Treasury, South Africa},
  year         = {2026},
  url          = {https://municipaldata.treasury.gov.za/docs#cube-aged_creditor_v2},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-south-africa-aged-creditor-analysis-v2-2026-48af2506}}
}

License

Released under Other open license.

Original data (c) National Treasury, South Africa. When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging.

About Electric Sheep

Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on Hugging Face. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepafrica

---

Provenance: ingested 2026-07-22 via the Electric Sheep pipeline. Source URL: https://munimoney-media.s3.eu-west-1.amazonaws.com/munimoney-media/media/bulk_downloads/aged_creditor_facts_v2/aged_creditor_facts_v2_2026-07-06_13-35-34.csv