electricsheepafrica/africa-cod-ps-zaf

South Africa - Subnational Population Statistics

Publisher: UNFPA · Source: HDX · License: cc-by-igo · Updated: 2025-04-08

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Abstract

South Africa administrative levels 0 (country), 1 (province), 2 (district), and 3 (local municipality) population statistics.

REFERENCE YEAR: 2016

These CSV files are suitable for database or GIS linkage to the South Africa - Subnational Administrative Boundaries shapefiles.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-04-08. Geographic scope: ZAF.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Demographics and population
Unit of observation	Tabular records
Rows (total)	215
Columns	2 (0 numeric, 2 categorical, 0 datetime)
Train split	172 rows
Test split	43 rows
Geographic scope	ZAF
Publisher	UNFPA
HDX last updated	2025-04-08

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Variables

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-04).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-cod-ps-zaf")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-04

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Numeric Summary

Column	Min	Max	Mean	Median
No numeric columns.

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Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. 57 column(s) with >80% missing values were removed: adm0_en, adm0_pcode, year, f_tl, m_tl, t_tl.... The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from UNFPA and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_cod_ps_zaf,
  title     = {South Africa - Subnational Population Statistics},
  author    = {UNFPA},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/cod-ps-zaf},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.