electricsheepafrica/africa-egypt-capmas-public-transportation-for-passengers-intra-and-inter-cities-ed12bb3e

Public Transportation for Passengers Intra- and Inter-Cities | Africa (CAPMAS Egypt Open Data)

9,134 rows - 1 Africa country/area - 2010-2022 - 115 indicators - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

TL;DR

This dataset contains 9,134 rows from CAPMAS Egypt Open Data, covering Public Transportation for Passengers Intra- and Inter-Cities. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Transport datasets help analysts examine mobility, infrastructure, passenger movement, logistics, and access to services.

This dataset covers Public Transportation for Passengers Intra- and Inter-Cities 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

• —2315 - Lengths of operational lines by regions (outside cities)(K.m)
• —1500 - Lengths of operational bus lines in provincial cities(K.m)
• —1812 - Lengths of operational lines according to the type of transport (within Alexandria)(K.m)
• —1705 - Lengths of operational lines according to the type of transport (within Cairo)(K.m)
• —2402 - Total Length of Railway Lines (Outside Cities)(K.m)
• —2407 - Total Length of Railways (Outside Cities)(K.m)
• —2099 - Total number of operational vehicles according to public passenger transport companies distributed by regions outside the cities.(Number)
• —2141 - Total number of vehicles owned by public transport authorities, companies, and facilities for passenger transport(Number)
• —2119 - Total number of vehicles owned by public passenger transport companies distributed by regions outside the cities(Number)
• —1743 - Total number of vehicles owned by public transport authorities, companies, and facilities for passenger transport, distributed by type of vehicle.(Number)
• —1885 - Total cash wages, in-kind benefits, and social insurance according to regions and centers of operation (outside cities)(By 1000 L.E)
• —2411 - Total number of trips for operating trains on railways (outside cities)(Number)
• —2300 - Total cash wages, in-kind benefits, and social insurance according to the type of transport and operating centers (within Alexandria)(By 1000 L.E)
• —2277 - Total cash wages, in-kind benefits, and social insurance according to the type of transport and operating centers (within Cairo)(By 1000 L.E)
• —2308 - Total Movement of Fixed Assets (Outside Cities)(By 1000 L.E)
• —1794 - Total cash wages, in-kind benefits, and social insurance according to operating centers of public buses in the governorates.(By 1000 L.E)
• —2413 - Total Number of Railway Lines (Outside Cities)(Number)
• —2319 - Total value of fuel, oils, and greases consumed in the operation of public passenger transport authorities, companies, and facilities.(By 1000 L.E)
• —2325 - Total cash wages, social insurance, and in-kind benefits for employees in public passenger transport authorities, companies, and facilities.(By 1000 L.E)
• —1583 - Total number of vehicles owned by public transport authorities, companies, and facilities for passenger transport according to the cities of the governorates.(Number)
• —... 95 more indicators

Schema

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-egypt-capmas-public-transportation-for-passengers-intra-and-inter-cities-ed12bb3e")
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: Public Transportation for Passengers Intra- and Inter-Cities
• —License: Source-specific or other license
• —Retrieved/generated: 2026-08-08T13:11:16Z
• —Hugging Face repo: electricsheepafrica/africa-egypt-capmas-public-transportation-for-passengers-intra-and-inter-cities-ed12bb3e

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 mobility over time
• —Compare routes or geographies
• —Join with economic and population 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_public_transportation_for_passengers_intra_and_inter_cities_2022,
  title        = {Public Transportation for Passengers Intra- and Inter-Cities | Africa (CAPMAS Egypt Open Data)},
  author       = {Central Agency for Public Mobilisation and Statistics (CAPMAS), Egypt},
  year         = {2022},
  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-public-transportation-for-passengers-intra-and-inter-cities-ed12bb3e}}
}

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