electricsheepafrica/africa-egypt-capmas-foreign-employees-in-governmental-public-public-business-sectors-9a2bee1c

Foreign Employees in Governmental & Public/Public Business Sectors | Africa (CAPMAS Egypt Open Data)

4,012 rows - 1 Africa country/area - 2010-2023 - 49 indicators - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

TL;DR

This dataset contains 4,012 rows from CAPMAS Egypt Open Data, covering Foreign Employees in Governmental & Public/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

Official statistics datasets help analysts inspect public data as published by governments, national statistical systems, and regional data portals.

This dataset covers Foreign Employees in Governmental & Public/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

• —2729 - Total foreign workers in the government sector and public sector/state-owned enterprises according to the place of work and Oceanic countries.(Number)
• —2634 - Total Foreign Workers in Ministries(Number)
• —2705 - Development of the Number of Foreign Workers in the Government Sector and the Public Sector / Public Business(Number)
• —2636 - Total Foreign Workers in Ministries by Type of Contract(Number)
• —2638 - Total Foreign Workers in Public Authorities by Educational Status(Number)
• —2637 - Total Foreign Workers in Public Authorities(Number)
• —2640 - Total Foreign Workers in Public Authorities According to Major Occupational Categories(Number)
• —2639 - Total foreign workers in public authorities with university degrees and higher according to scientific groups(Number)
• —2641 - Total Foreign Workers in Public Authorities by Type of Contract(Number)
• —2642 - Total Foreign Workers in the Governorates(Number)
• —2643 - Total Foreign Workers in the Governorates According to the Groups of Countries They Come From(Number)
• —2644 - Total Foreign Workers in the Governorates by Educational Status(Number)
• —2646 - Total foreign workers in the governorates with university degrees or higher(Number)
• —2645 - Total foreign workers in the governorates with university degrees or higher, categorized by scientific groups.(Number)
• —2647 - Foreign workers in the governorates according to major occupational categories(Number)
• —2648 - Total Foreign Workers in the Governorates by Type of Contract(Number)
• —2653 - Total Foreign Workers in Public Sector/State-Owned Enterprises by Educational Attainment(Number)
• —2657 - Total number of foreign workers in public sector companies / public business sector with university degrees and higher according to scientific groups.(Number)
• —2649 - Total Foreign Workers in Public Sector/State-Owned Enterprises by Country of Origin(Number)
• —2650 - Total Foreign Workers in Public Sector / Public Business Sector Companies(Number)
• —... 29 more indicators

Schema

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-egypt-capmas-foreign-employees-in-governmental-public-public-business-sectors-9a2bee1c")
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: Foreign Employees in Governmental & Public/Public Business Sectors
• —License: Source-specific or other license
• —Retrieved/generated: 2026-08-08T13:12:53Z
• —Hugging Face repo: electricsheepafrica/africa-egypt-capmas-foreign-employees-in-governmental-public-public-business-sectors-9a2bee1c

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

• —Profile the distribution of values
• —Compare categories or geographies
• —Join with complementary public datasets
• —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_foreign_employees_in_governmental_public_public_business_sec_2023,
  title        = {Foreign Employees in Governmental & Public/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-foreign-employees-in-governmental-public-public-business-sectors-9a2bee1c}}
}

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