electricsheepafrica/africa-egypt-capmas-workers-in-the-public-sector-and-public-business-sectors-fe8d0c3b

Workers in the Public Sector and Public Business Sectors | Africa (CAPMAS Egypt Open Data)

2,715 rows - 1 Africa country/area - 2010-2023 - 45 indicators - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

TL;DR

This dataset contains 2,715 rows from CAPMAS Egypt Open Data, covering Workers in the Public Sector and Public Business Sectors. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Labour and workforce datasets help analysts study employment, participation, skills, sectoral structure, and the movement of people through work and livelihoods.

This dataset covers Workers in the Public Sector and Public Business Sectors from CAPMAS Egypt Open Data. Use the source and schema sections below to confirm definitions, units, and collection methodology before sensitive analytical use.

How To Read This Dataset

• —One row means: one indicator observation for one geography, time period, and optional source dimensions.
• —Primary geography column: source metadata (EGY).
• —Best time column: year.
• —Time coverage basis: year.
• —Recommended join keys: year, indicator_id, plus source-specific dimension columns.

Coverage

Geographic Coverage

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

Indicators, Variables, Or Resource Contents

• —10 - Distribution of employees not currently working who are on missions, scholarships, secondments, and study leaves.(Number)
• —14 - Distribution of skilled workers and civilian auxiliary services in the public sector / public business according to educational status(Number)
• —75 - Distribution of government employees across the governorates(Number)
• —9 - Distribution of civilian workers in Public/Public Business Sector according to governorates(Number)
• —8 - Distribution of civilian workers in Public/Public business sector by educational status(Number)
• —7 - Distribution of civilian workers in Public/Public business sector by financial grade(Number)
• —7397 - Distribution of civil servants in the public sector / public business sector who have left service according to financial grade(Number)
• —13 - Distribution of skilled workers and civilian auxiliary services in the public sector / public business according to gender(Number)
• —18 - Distribution of Civil Servants in the Public Sector / Public Business Sector by Gender(Number)
• —15 - Distribution of civilian employees in the public sector / public business according to qualitative groups(Number)
• —72 - Distribution of civil servants in the public sector / public business sector who left service by gender(Number)
• —19 - Distribution of government employees by gender in the public budget(Number)
• —4 - Distribution of civilian workers in Public/Public Business Sector who left service according to leaving the service reason(Number)
• —20 - Distribution of government employees in the public budget according to the type of cadre(Number)
• —11 - Distribution of civil servants in the public sector / public business according to the economic sector(Number)
• —97 - Distribution of inactive workers by gender(Number)
• —40 - Distribution of female employees in the government sector according to the financial grade for the budgets of economic authorities.(Number)
• —99 - Distribution of government employees in the economic authorities' budgets by gender(Number)
• —3 - Relative distribution of government employees in the public budget according to different grades(Percentage to the funded)
• —16 - Distribution of government employees according to different sectors in the budgets of the administrative apparatus, local administration, and service authorities.(Number)
• —... 25 more indicators

Schema

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-egypt-capmas-workers-in-the-public-sector-and-public-business-sectors-fe8d0c3b")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

print(df.info())
print(df.head())

Filter By Geography

# This dataset is scoped to Egypt in source metadata.
sample = df.copy()

Time-Series Pattern

if "value" in df.columns and "year" in df.columns:
    trend = df.sort_values("year")

Pivot For Analysis

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: CAPMAS Egypt Open Data
• —Publisher: Central Agency for Public Mobilisation and Statistics (CAPMAS), Egypt
• —Portal: https://www.capmas.gov.eg
• —Resource: Workers in the Public Sector and Public Business Sectors
• —License: Source-specific or other license
• —Retrieved/generated: 2026-08-08T13:12:51Z
• —Hugging Face repo: electricsheepafrica/africa-egypt-capmas-workers-in-the-public-sector-and-public-business-sectors-fe8d0c3b

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

• —Track workforce composition over time
• —Compare employment patterns across groups
• —Join with education, population, and sector data
• —Build time-series views and period-over-period comparisons
• —Pivot to indicator x period matrices
• —Check missingness before modeling
• —Use source or repo metadata for country scope when a geography column is not present

Citation

@misc{electric_sheep_africa_africa_egypt_capmas_workers_in_the_public_sector_and_public_business_sectors_fe8_2023,
  title        = {Workers in the Public Sector and Public Business Sectors | Africa (CAPMAS Egypt Open Data)},
  author       = {Central Agency for Public Mobilisation and Statistics (CAPMAS), Egypt},
  year         = {2023},
  url          = {https://www.capmas.gov.eg},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-egypt-capmas-workers-in-the-public-sector-and-public-business-sectors-fe8d0c3b}}
}

License

Released under Source-specific or other license.

Original data is published by Central Agency for Public Mobilisation and Statistics (CAPMAS), Egypt. 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 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://www.capmas.gov.eg