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electricsheepafrica/africa-egypt-capmas-postal-services-bbb95d68

Postal Services | Africa (CAPMAS Egypt Open Data) 4,227 rows - 1 Africa country/area - 2010-2023 - 45 indicators - Engineered by Electric Sheep Africa TL;DR This dataset contains 4,227 rows from CAPMAS Egypt Open Data, covering Postal Services. 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… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-egypt-capmas-postal-services-bbb95d68.

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Postal Services | Africa (CAPMAS Egypt Open Data)

4,227 rows - 1 Africa country/area - 2010-2023 - 45 indicators - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 4,227 rows from CAPMAS Egypt Open Data, covering Postal Services. 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 Postal Services 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

DimensionValue
Rows4,227
Countries/areas1
First period2010
Last period2023
Indicators45
Columns12
Source formatPARQUET

Geographic Coverage

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

AreaRowsFirst yearLast yearName
EGY4,22720102023Egypt

Indicators, Variables, Or Resource Contents

  • —2544 - Total Revenue from Activities(EGP Thousand)
  • —2604 - Total Number of Special Bags in Postal Units(Number)
  • —2546 - Total Number of ATM Services at Postal Units Distributed by Service Type(Number)
  • —2547 - Total number of services provided by postal units distributed by type of service(Number)
  • —2512 - Total number of technical means used in postal units(Number)
  • —2561 - Total Cash Movement of Postal Checks(EGP Thousand)
  • —2558 - Total Number of P.O.S Services at Postal Units Distributed by Service Type(Number)
  • —2527 - Total Number of Postal Service Units(Number)
  • —2540 - Total Value of Consumed Goods Supplies(EGP Thousand)
  • —2538 - Total Value of Service Expenses(EGP Thousand)
  • —2550 - Total Value of Services Provided by Postal Units Distributed by Type of Service(EGP Thousand)
  • —2597 - Total Value of P.O.S Services in Postal Units Distributed by Service Type(EGP Thousand)
  • —9006 - Outgoing External Correspondence Movement by Postal Units According to Type of Correspondence(No. in 1000)
  • —9007 - Incoming External Correspondence Movement in Postal Units by Type of Correspondence(No. in 1000)
  • —2554 - Number of Regular Domestic and International Parcels(Number)
  • —2563 - Number of outgoing internal correspondences in government units distributed by type(No. in 1000)
  • —2531 - Number of postal transfers distributed by type(Number)
  • —2564 - Number of incoming internal correspondences in government units distributed by type(No. in 1000)
  • —2601 - Value of postal remittances distributed by type(EGP Thousand)
  • —2583 - Cash Flow of the Savings Fund(EGP Thousand)
  • —... 25 more indicators

Schema

ColumnTypeDescriptionExample
filter_idstringSource column from the original resource.690
filter_arstringSource column from the original resource.خدمات مباعة
filter_enstringSource column from the original resource.Sold services
valuedoubleNumeric observation value.72869.0
yearint64Observation year.2013
quarternullSource column from the original resource.``
monthnullSource column from the original resource.``
indicator_idstringStable source or Electric Sheep Africa indicator identifier.2544
indicator_namestringHuman-readable indicator name.Total Revenue from Activities
indicator_name_arstringSource column from the original resource.اجمالي إيرادات النشاط
unitstringMeasurement unit, when supplied by the source.EGP Thousand
periodicitystringSource column from the original resource.Annually

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-egypt-capmas-postal-services-bbb95d68")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

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

Filter By Geography

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

Time-Series Pattern

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

Pivot For Analysis

python
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

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

bibtex
@misc{electric_sheep_africa_africa_egypt_capmas_postal_services_bbb95d68_2023,
  title        = {Postal Services | 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-postal-services-bbb95d68}}
}

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