electricsheepafrica/africa-employment-all

Quarterly Labour Force Survey Q4: 2024

Publisher: Statistics South Africa · Source: OpenAfrica · License: cc-by · Updated: 2025-02-27

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Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (Stats SA). It collects data on the labour market activities of individuals aged 15 years and older who live in South Africa. This data covers labour market activities of persons aged 15–64 years: key findings of the QLFS conducted from October to December 2024 (Q4: 2024).

Each row in this dataset represents time-series observations. Data was last updated on OpenAfrica on 2025-02-27. Geographic scope: Africa (multiple countries).

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

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Dataset Characteristics

Domain	Market and price monitoring
Unit of observation	Time-series observations
Rows (total)	34
Columns	12 (9 numeric, 3 categorical, 0 datetime)
Train split	27 rows
Test split	6 rows
Geographic scope	Africa (multiple countries)
Publisher	Statistics South Africa
OpenAfrica last updated	2025-02-27

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Variables

Geographic — year_on_year_change (range -29.0–110.0), year_on_year_change_1 (range -21.9–185.9).

Identifier / Metadata — unnamed_0 (Men, Women, Both sexes), esa_source (HDX), esa_processed (2026-04-27).

Other — oct_dec_2023 (range 1.0–678.0), jan_mar_2024 (range 2.5–722.0), apr_jun_2024 (range 1.0–677.0), jul_sep_2024 (range 1.0–783.0), oct_dec_2024 (range 2.8–788.0) and 2 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-employment-all")
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
unnamed_0	object	2.9%	Men, Women, Both sexes
oct_dec_2023	float64	11.8%	1.0 – 678.0 (mean 136.26)
jan_mar_2024	float64	14.7%	2.5 – 722.0 (mean 150.031)
apr_jun_2024	float64	11.8%	1.0 – 677.0 (mean 136.09)
jul_sep_2024	float64	8.8%	1.0 – 783.0 (mean 152.2774)
oct_dec_2024	float64	14.7%	2.8 – 788.0 (mean 163.8759)
qtr_to_qtr_change	float64	14.7%	-33.0 – 29.0 (mean 1.1655)
year_on_year_change	float64	14.7%	-29.0 – 110.0 (mean 22.9862)
qtr_to_qtr_change_1	float64	32.4%	-23.9 – 59.5 (mean 5.4435)
year_on_year_change_1	float64	32.4%	-21.9 – 185.9 (mean 44.7913)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-27

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Numeric Summary

Column	Min	Max	Mean	Median
oct_dec_2023	1.0	678.0	136.26	22.5
jan_mar_2024	2.5	722.0	150.031	38.0
apr_jun_2024	1.0	677.0	136.09	26.5
jul_sep_2024	1.0	783.0	152.2774	30.0
oct_dec_2024	2.8	788.0	163.8759	43.0
qtr_to_qtr_change	-33.0	29.0	1.1655	0.2
year_on_year_change	-29.0	110.0	22.9862	12.0
qtr_to_qtr_change_1	-23.9	59.5	5.4435	0.6
year_on_year_change_1	-21.9	185.9	44.7913	26.6

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Curation

Raw data was downloaded from OpenAfrica 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. 3 exact duplicate rows were removed. 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 Statistics South Africa and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• The following columns have >20% missing values and should be treated with caution in modelling: qtr_to_qtr_change_1, year_on_year_change_1.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{openafrica_africa_employment_all,
  title     = {Quarterly Labour Force Survey Q4: 2024},
  author    = {Statistics South Africa},
  year      = {2025},
  url       = {https://open.africa/dataset/qlfsq42024},
  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.