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electricsheepafrica
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africa-morocco-archive-2016-statistiques-hebdomadaires-des-organismes-de-d4bfc4f1

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Tabular Classification
Tabular Regression
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tabular
xls
africa
morocco
official-statistics
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metadata 
license: odbl
language:
  - en
task_categories:
  - tabular-classification
  - tabular-regression
multilinguality: monolingual
size_categories:
  - n<1K
tags:
  - tabular
  - xls
  - africa
  - morocco
  - official-statistics
  - open-data
pretty_name: >-
  [Archive 2016] Statistiques hebdomadaires des Organismes de Placement
  Collectif en Valeurs Mobilières (OPCVM) pour l'année 2016 | Africa (Morocco
  official open data)

[Archive 2016] Statistiques hebdomadaires des Organismes de Placement Collectif en Valeurs Mobilières (OPCVM) pour l'année 2016 | Africa (Morocco official open data)

42 rows - 1 Africa country - not-applicable - Repackaged by Electric Sheep Africa

rows countries years indicators license

TL;DR

This dataset packages one official XLS resource from Morocco as ML-ready Parquet. The source file is the provenance boundary; all usable indicators or tabular columns from the resource stay together in this repo.

About the source

Geographic coverage

1 Africa country:

Country Rows First year Last year Name
MAR 42 n/a n/a Morocco

Indicators or Resource Contents

  • This source file is packaged as a normalized tabular resource.

Schema

Column Type Description Example
source_record_id string Stable row identifier for tabular resources. f3c28280-7c84-444f-9919-d9e05f4debe1:francais:0
country_iso3 string ISO3 country code. MAR
country_name string Country name. Morocco
source_sheet string Workbook sheet name, when the source is a spreadsheet. Français
categorie string Source column. Actions
nombre_opcvm float64 Source column. 88.0
montant string Source column. 24891256159.7967
structure string Source column. 6.97526917397302
variation_hebdomadaire float64 Source column. 3.67978022724771
variation_mensuelle float64 Source column. 6.36241603486844
variation_annuelle float64 Source column. 21.7996513188209
column_8 string Source column. ``
source_provider string Publishing organization. AMMC
source_dataset string Source package title. [Archive 2016] Statistiques hebdomadaires des Organismes de Placement Co
source_resource string Source resource title. STAT_OPCVM_HEBDO_16122016.xls
source_package_id string CKAN package UUID. a6c0a987-3607-4a17-99a7-5a288edf2da8
source_resource_id string CKAN resource UUID. f3c28280-7c84-444f-9919-d9e05f4debe1
source_url string Original source resource URL. https://data.gov.ma/data/fr/dataset/a6c0a987-3607-4a17-99a7-5a288edf2da8
license_id string Source license identifier. odc-odbl
retrieved_at string UTC retrieval timestamp. 2026-07-16T21:31:48Z

Usage 

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-morocco-archive-2016-statistiques-hebdomadaires-des-organismes-de-d4bfc4f1")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country 

sample_country = df[df["country_iso3"] == "MAR"]

Work with indicators 

if "indicator_id" in df.columns:
    print(df["indicator_id"].value_counts().head())
    sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns])

Citation 

@misc{electric_sheep_africa_africa_morocco_archive_2016_statistiques_hebdomadaires_des_organismes_de_d4bfc4f_2026,
  title        = {[Archive 2016] Statistiques hebdomadaires des Organismes de Placement Collectif en Valeurs Mobilières (OPCVM) pour l'année 2016 | Africa (Morocco official open data)},
  author       = {AMMC},
  year         = {2026},
  url          = {https://data.gov.ma/data/dataset/stat-hebdo-opcvm-2016},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-morocco-archive-2016-statistiques-hebdomadaires-des-organismes-de-d4bfc4f1}}
}

License

Released under Open Data Commons Open Database License.

Original data (c) AMMC. When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging.

About Electric Sheep

Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on Hugging Face. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepafrica

Provenance: ingested 2026-07-16 via the Electric Sheep pipeline. Source URL: https://data.gov.ma/data/fr/dataset/a6c0a987-3607-4a17-99a7-5a288edf2da8/resource/f3c28280-7c84-444f-9919-d9e05f4debe1/download/stat_opcvm_hebdo_16122016.xls