--- license: odbl language: - en task_categories: - tabular-classification - tabular-regression multilinguality: monolingual size_categories: - n<1K tags: - tabular - xlsx - africa - morocco - official-statistics - open-data pretty_name: "Affaires pénales enregistrées en 2022 | Africa (Morocco official open data)" --- # Affaires pénales enregistrées en 2022 | Africa (Morocco official open data) 157 rows - 1 Africa country - not-applicable - Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica) ![rows](https://img.shields.io/badge/rows-157-blue) ![countries](https://img.shields.io/badge/countries-1-green) ![years](https://img.shields.io/badge/years-not-applicable-orange) ![indicators](https://img.shields.io/badge/indicators-0-purple) ![license](https://img.shields.io/badge/license-odbl-lightgrey) ## TL;DR This dataset packages one official `XLSX` 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 - **Source:** [Affaires pénales enregistrées en 2022](https://data.gov.ma/data/dataset/affaires-penales-enregistrees-en-2022) - **Publisher:** MJ - **Resource:** [القضايا الزجرية المسجلة سنة 2022.xlsx](https://data.gov.ma/data/fr/dataset/8efec2af-1b5d-4931-85ee-049ce8607997/resource/68c53513-bf64-459d-8b2b-65db1295b7cf/download/-2022.xlsx) - **Format:** `XLSX` - **License:** [Open Data Commons Open Database License](https://opendatacommons.org/licenses/odbl/) - **Packaging mode:** `tabular_resource` ## Geographic coverage 1 Africa country: | Country | Rows | First year | Last year | Name | |---------|-----:|-----------:|----------:|------| | `MAR` | 157 | 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. | `68c53513-bf64-459d-8b2b-65db1295b7cf:feuil1:0` | | `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. | `Feuil1` | | `column` | `string` | Source column. | `المحكمة الابتدائية المدنية بالدار البيضاء` | | `column_2` | `float64` | Source column. | `` | | `column_3` | `float64` | Source column. | `` | | `column_4` | `float64` | Source column. | `` | | `column_5` | `float64` | Source column. | `` | | `column_6` | `float64` | Source column. | `` | | `column_7` | `float64` | Source column. | `` | | `column_8` | `float64` | Source column. | `` | | `column_9` | `float64` | Source column. | `` | | `column_10` | `int64` | Source column. | `0` | | `source_provider` | `string` | Publishing organization. | `MJ` | | `source_dataset` | `string` | Source package title. | `Affaires pénales enregistrées en 2022` | | `source_resource` | `string` | Source resource title. | `القضايا الزجرية المسجلة سنة 2022.xlsx` | | `source_package_id` | `string` | CKAN package UUID. | `8efec2af-1b5d-4931-85ee-049ce8607997` | | `source_resource_id` | `string` | CKAN resource UUID. | `68c53513-bf64-459d-8b2b-65db1295b7cf` | | `source_url` | `string` | Original source resource URL. | `https://data.gov.ma/data/fr/dataset/8efec2af-1b5d-4931-85ee-049ce8607997` | | `license_id` | `string` | Source license identifier. | `odc-odbl` | | `retrieved_at` | `string` | UTC retrieval timestamp. | `2026-07-16T21:31:48Z` | ## Usage ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-morocco-affaires-penales-enregistrees-en-2022-41b93e24") df = ds["train"].to_pandas() print(df.head()) ``` ### Filter to one country ```python sample_country = df[df["country_iso3"] == "MAR"] ``` ### Work with indicators ```python 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 ```bibtex @misc{electric_sheep_africa_africa_morocco_affaires_penales_enregistrees_en_2022_41b93e24_2026, title = {Affaires pénales enregistrées en 2022 | Africa (Morocco official open data)}, author = {MJ}, year = {2026}, url = {https://data.gov.ma/data/dataset/affaires-penales-enregistrees-en-2022}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-morocco-affaires-penales-enregistrees-en-2022-41b93e24}} } ``` ## License Released under [Open Data Commons Open Database License](https://opendatacommons.org/licenses/odbl/). Original data (c) MJ. 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](https://huggingface.co/electricsheepafrica) --- Provenance: ingested 2026-07-16 via the Electric Sheep pipeline. Source URL: https://data.gov.ma/data/fr/dataset/8efec2af-1b5d-4931-85ee-049ce8607997/resource/68c53513-bf64-459d-8b2b-65db1295b7cf/download/-2022.xlsx