CoolFace
Datasetpublic

electricsheepafrica/africa-egypt-capmas-individual-savings-in-major-saving-vehicles-ce26ece0

Individual Savings in Major Saving Vehicles | Africa (CAPMAS Egypt Open Data) 935 rows - 1 Africa country/area - 2010-2022 - 13 indicators - Engineered by Electric Sheep Africa TL;DR This dataset contains 935 rows from CAPMAS Egypt Open Data, covering Individual Savings in Major Saving Vehicles. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples. What This Dataset… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-egypt-capmas-individual-savings-in-major-saving-vehicles-ce26ece0.

sourceHugging Faceotherupdated 2mo agoView on Hugging Face
0likes44downloads
Dataset Card

Individual Savings in Major Saving Vehicles | Africa (CAPMAS Egypt Open Data)

935 rows - 1 Africa country/area - 2010-2022 - 13 indicators - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 935 rows from CAPMAS Egypt Open Data, covering Individual Savings in Major Saving Vehicles. 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 Individual Savings in Major Saving Vehicles 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
Rows935
Countries/areas1
First period2010
Last period2022
Indicators13
Columns12
Source formatPARQUET

Geographic Coverage

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

AreaRowsFirst yearLast yearName
EGY93520102022Egypt

Indicators, Variables, Or Resource Contents

  • —8984 - Number of New Banking Saving Accounts in both (Public and Private) Sectors During the Year(Number)
  • —1969 - Percentage of Individual Savings in Banking Saving Vehicles(Percentage)
  • —1973 - Total Percentage of Individual Savings in Banking Saving Vehicles to Gross Domestic Product including Cost of Factor Production(Percentage)
  • —1977 - Total Individual Savings in Banking Saving Vehicles During a Year(EGP Thousand)
  • —1978 - Savings Balance in Banking Saving Vehicles at the End of the Year(EGP Thousand)
  • —1980 - Total Movement of Individual Savings in Banking Saving Vehicles to Gross Domestic Product including Cost of Factor Production(EGP Thousand)
  • —1967 - Net Individual Savings in Banking Saving Vehicles(EGP Thousand)
  • —1994 - Total Saving Vehicles According to Type of Investments(EGP Thousand)
  • —1998 - Relative Importance of Distribution of Investment Balances for Saving Authorities and Insurance Companies According to Saving Vehicle(Percentage)
  • —2006 - Total Distribution of Investment Balances on Saving Authorities and Insurancee Companies According to Saving Vehicles(EGP Thousand)
  • —8986 - Number of Banking Saving Accounts in both (Public and Private) Sectors by the End of the Year(Number)
  • —8985 - Individual Savings in Banking Saving Vehicles in both (Public and Private) Sectors by the End of the Year(EGP Thousand)
  • —1981 - Total Distribution of Individual Saving Vehicles(EGP Thousand)

Schema

ColumnTypeDescriptionExample
filter_idstringSource column from the original resource.60
filter_arstringSource column from the original resource.الإجمالي
filter_enstringSource column from the original resource.Total
valuedoubleNumeric observation value.94475723.0
yearint64Observation year.2010
quarternullSource column from the original resource.``
monthnullSource column from the original resource.``
indicator_idstringStable source or Electric Sheep Africa indicator identifier.8984
indicator_namestringHuman-readable indicator name.Number of New Banking Saving Accounts in both (Public and Private) Se...
indicator_name_arstringSource column from the original resource.عدد الحسابات لدى أوعية الإدخار الرئيسية القطاعين (العام والخاص) الجدد...
unitstringMeasurement unit, when supplied by the source.Number
periodicitystringSource column from the original resource.Annually

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-egypt-capmas-individual-savings-in-major-saving-vehicles-ce26ece0")
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

  • —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

bibtex
@misc{electric_sheep_africa_africa_egypt_capmas_individual_savings_in_major_saving_vehicles_ce26ece0_2022,
  title        = {Individual Savings in Major Saving Vehicles | 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-individual-savings-in-major-saving-vehicles-ce26ece0}}
}

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