electricsheepafrica/africa-morocco-liaisons-data-entreprises-92c88059

Liaisons Data Entreprises | Africa (Morocco Open Data)

204 rows - 1 Africa country/area - 2018-2022 - 12 indicators - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

TL;DR

This dataset contains 204 rows from Morocco Open Data, covering Liaisons Data Entreprises. 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: Informations sur les liaisons Data Entreprises de 2018 au 2026

How To Read This Dataset

• —One row means: one indicator observation for one geography, time period, and optional source dimensions.
• —Primary geography column: country_iso3.
• —Best time column: year.
• —Time coverage basis: year.
• —Recommended join keys: country_iso3, year, indicator_id.

Coverage

Geographic Coverage

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

Indicators, Variables, Or Resource Contents

• —liaisons-data-entreprises-a-e3a08c9e - Liaisons Data Entreprises - a(sourceunitsunspecified)
• —liaisons-data-entreprises-b-530e0aab - Liaisons Data Entreprises - b(sourceunitsunspecified)
• —liaisons-data-entreprises-c-e79cd49d - Liaisons Data Entreprises - c(sourceunitsunspecified)
• —liaisons-data-entreprises-d-2f5e8dd3 - Liaisons Data Entreprises - d(sourceunitsunspecified)
• —liaisons-data-entreprises-e-b22022ed - Liaisons Data Entreprises - e(sourceunitsunspecified)
• —liaisons-data-entreprises-f-be839734 - Liaisons Data Entreprises - f(sourceunitsunspecified)
• —liaisons-data-entreprises-g-ff3a7890 - Liaisons Data Entreprises - g(sourceunitsunspecified)
• —liaisons-data-entreprises-h-7edb673e - Liaisons Data Entreprises - h(sourceunitsunspecified)
• —liaisons-data-entreprises-i-812ac001 - Liaisons Data Entreprises - i(sourceunitsunspecified)
• —liaisons-data-entreprises-j-8bd0a7c1 - Liaisons Data Entreprises - j(sourceunitsunspecified)
• —liaisons-data-entreprises-k-c0f295f5 - Liaisons Data Entreprises - k(sourceunitsunspecified)
• —liaisons-data-entreprises-l-536da091 - Liaisons Data Entreprises - l(sourceunitsunspecified)

Schema

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-morocco-liaisons-data-entreprises-92c88059")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

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

Filter By Geography

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

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: Morocco Open Data
• —Publisher: ANRT
• —Portal: https://data.gov.ma/data
• —Resource: Data Entreprises.xlsx
• —License: Open Data Commons Open Database License
• —Retrieved/generated: 2026-07-16T21:34:39Z
• —Hugging Face repo: electricsheepafrica/africa-morocco-liaisons-data-entreprises-92c88059

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 geography x period or indicator x period matrices
• —Check missingness before modeling
• —Use country_iso3 as the safest geography join key when present

Citation

@misc{electric_sheep_africa_africa_morocco_liaisons_data_entreprises_92c88059_2022,
  title        = {Liaisons Data Entreprises | Africa (Morocco Open Data)},
  author       = {ANRT},
  year         = {2022},
  url          = {https://data.gov.ma/data/dataset/liaisons-data-entreprises-2018-2022},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-morocco-liaisons-data-entreprises-92c88059}}
}

License

Released under Open Data Commons Open Database License.

Original data is published by ANRT. 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/liaisons-data-entreprises-2018-2022