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electricsheepafrica/africa-morocco-repartition-du-nombre-des-entreprises-declarantes-aupres-d-5ec6dd2e

Repartition Du Nombre Des Entreprises Declarantes Aupres D | Africa (Morocco Open Data) 25 rows - 1 Africa country/area - time not specified - source table - Engineered by Electric Sheep Africa TL;DR This dataset contains 25 rows from Morocco Open Data, covering Repartition Du Nombre Des Entreprises Declarantes Aupres D. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-morocco-repartition-du-nombre-des-entreprises-declarantes-aupres-d-5ec6dd2e.

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Dataset Card

Repartition Du Nombre Des Entreprises Declarantes Aupres D | Africa (Morocco Open Data)

25 rows - 1 Africa country/area - time not specified - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 25 rows from Morocco Open Data, covering Repartition Du Nombre Des Entreprises Declarantes Aupres D. 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.

Source-provided context: Ce fichier présente la répartition du nombre des entreprises déclarantes auprès de la CNSS, par région administrative et par secteur d'activité, au titre de l'année 2021

How To Read This Dataset

  • —One row means: one source record from the original tabular resource, with Electric Sheep Africa provenance columns added where available.
  • —Primary geography column: country_iso3.
  • —Best time column: not detected.
  • —Time coverage basis: not detected.
  • —Recommended join keys: country_iso3 where available plus source-specific keys.

Coverage

DimensionValue
Rows25
Countries/areas1
First periodn/a
Last periodn/a
Indicators0
Columns15
Source formatXLSX

Geographic Coverage

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

AreaRowsFirst yearLast yearName
MAR25n/an/aMorocco

Indicators, Variables, Or Resource Contents

  • —This repo preserves one source tabular resource with its usable columns kept together.

Schema

ColumnTypeDescriptionExample
source_record_idstringStable row identifier assigned during Electric Sheep Africa engineering.0b4ef808-a741-4249-a509-39866243fabc:nbr-des-entreprises-declarantes:0
country_iso3stringISO3 country or area code.MAR
country_namestringCountry or area name.Morocco
source_sheetstringSource column from the original resource.Nbr des entreprises déclarantes
marrakech_safistringSource column from the original resource.Rabat-Salé-Kénitra
d_27579doubleSource column from the original resource.43132.0
d_0_09doubleSource column from the original resource.0.15
source_providerstringPublishing organization.CNSS
source_datasetstringSource dataset or package title.Répartition du nombre des entreprises déclarantes auprès de la CNSS e...
source_resourcestringSource resource title, table name, or file name.Répartition du nombre des entreprises déclarantes_2021.xlsx
source_package_idstringSource package identifier.f39e1324-8099-4604-bbf5-5bea64121e23
source_resource_idstringSource resource identifier.0b4ef808-a741-4249-a509-39866243fabc
source_urlstringOriginal source URL or download URL.https://data.gov.ma/data/fr/dataset/f39e1324-8099-4604-bbf5-5bea64121...
license_idstringSource license identifier.odc-odbl
retrieved_atstringUTC source retrieval timestamp from the Electric Sheep Africa pipeline.2026-07-16T21:31:48Z

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-morocco-repartition-du-nombre-des-entreprises-declarantes-aupres-d-5ec6dd2e")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

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

Filter By Geography

python
if "country_iso3" in df.columns:
    sample = df[df["country_iso3"] == "MAR"]

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

  • —No canonical year/date column was detected in the packaged table; use source metadata and domain context for temporal interpretation.
  • —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
  • —Check missingness before modeling
  • —Use country_iso3 as the safest geography join key when present

Citation

bibtex
@misc{electric_sheep_africa_africa_morocco_repartition_du_nombre_des_entreprises_declarantes_aupres_d_5ec6dd_2026,
  title        = {Repartition Du Nombre Des Entreprises Declarantes Aupres D | Africa (Morocco Open Data)},
  author       = {CNSS},
  year         = {2026},
  url          = {https://data.gov.ma/data/dataset/repartition-du-nombre-des-entreprises-declarantes-aupres-de-la-cnss-en-2021},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-morocco-repartition-du-nombre-des-entreprises-declarantes-aupres-d-5ec6dd2e}}
}

License

Released under Open Data Commons Open Database License.

Original data is published by CNSS. 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://data.gov.ma/data/dataset/repartition-du-nombre-des-entreprises-declarantes-aupres-de-la-cnss-en-2021