electricsheepafrica/africa-employment-all

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- employment
- south-africa
- underemployment
- labour
pretty_name: "Quarterly Labour Force Survey Q4: 2024"
dataset_info:
splits:
- name: train
num_examples: 27
- name: test
num_examples: 6
---

# Quarterly Labour Force Survey Q4: 2024

**Publisher:** Statistics South Africa · **Source:** [OpenAfrica](https://open.africa/dataset/qlfsq42024) · **License:** `cc-by` · **Updated:** 2025-02-27

---

## 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](https://huggingface.co/electricsheepafrica).*

---

## 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 |

---

## 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.

---

## Quick Start

```python
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()
```

---

## 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 |

---

## 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 |

---

## 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.

---

## 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](https://open.africa/dataset/qlfsq42024) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@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)}
}
```

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*