electricsheepafrica/africa-smiig-data-provinces-et-prefectures-2023

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- morocco
- open-data
- prefectures
- provinces
- right-to-access-information
- smiig-data
pretty_name: "SMIIG Data provinces et préfectures Maroc 2023"
dataset_info:
splits:
- name: train
num_examples: 60
- name: test
num_examples: 15
---

# SMIIG Data provinces et préfectures Maroc 2023

**Publisher:** TAFRA · **Source:** [OpenAfrica](https://open.africa/dataset/smiig-data-provinces-et-prefectures-2023) · **License:** `cc-by` · **Updated:** 2024-02-05

---

## Abstract

Données sur la mise en œuvre du droit d’accès à l’information par les provinces et les préfectures au Maroc en 2023.

Data on the implementation of the right of access to information by the provinces and prefectures in Morocco in 2023.

Each row in this dataset represents first-level administrative unit observations. Data was last updated on OpenAfrica on 2024-02-05. Geographic scope: **Africa (multiple countries)**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | First-level administrative unit observations |
| **Rows (total)** | 75 |
| **Columns** | 35 (29 numeric, 6 categorical, 0 datetime) |
| **Train split** | 60 rows |
| **Test split** | 15 rows |
| **Geographic scope** | Africa (multiple countries) |
| **Publisher** | TAFRA |
| **OpenAfrica last updated** | 2024-02-05 |

---

## Variables

**Geographic** — `idregion` (range 623.0–634.0), `idwilaya` (range 19.0–30.0), `region` (Fès - Meknès, Casablanca - Settat, Tanger - Tétouan - Al Hoceima), `wilaya` (Fès - Meknès, Casablanca - Settat, Tanger - Tétouan - Al Hoceima).

**Demographic** — `participation_agendaconseil` (range 0.0–13.0).

**Identifier / Metadata** — `idprefprov` (range 241.0–323.0), `prefprov` (Al Hoceima, Zagora, Errachidia), `esa_source` (HDX), `esa_processed` (2026-04-27).

**Other** — `url` (https://cptetouan.ma/, https://conseilprefectoralcasa.ma/, https://cp-tiznit.ma/), `annee` (range 2023.0–2023.0), `participation_contactaministration` (range 0.0–0.0), `participation_compositionconseil` (range 0.0–10.0), `participation_compositioncommissionsconseil` (range 0.0–10.0) and 21 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-smiig-data-provinces-et-prefectures-2023")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `idregion` | int64 | 0.0% | 623.0 – 634.0 (mean 627.5467) |
| `idwilaya` | int64 | 0.0% | 19.0 – 30.0 (mean 23.5467) |
| `idprefprov` | int64 | 0.0% | 241.0 – 323.0 (mean 285.4667) |
| `region` | object | 0.0% | Fès - Meknès, Casablanca - Settat, Tanger - Tétouan - Al Hoceima |
| `wilaya` | object | 0.0% | Fès - Meknès, Casablanca - Settat, Tanger - Tétouan - Al Hoceima |
| `prefprov` | object | 0.0% | Al Hoceima, Zagora, Errachidia |
| `url` | object | 74.7% | https://cptetouan.ma/, https://conseilprefectoralcasa.ma/, https://cp-tiznit.ma/ |
| `annee` | int64 | 0.0% | 2023.0 – 2023.0 (mean 2023.0) |
| `participation_contactaministration` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
| `participation_compositionconseil` | int64 | 0.0% | 0.0 – 10.0 (mean 1.7333) |
| `participation_agendaconseil` | int64 | 0.0% | 0.0 – 13.0 (mean 2.1333) |
| `participation_compositioncommissionsconseil` | int64 | 0.0% | 0.0 – 10.0 (mean 1.3333) |
| `participation_compositioninstancesconsultatives` | int64 | 0.0% | 0.0 – 10.0 (mean 0.4) |
| `participation_espaceconcertationenligne` | int64 | 0.0% | 0.0 – 10.0 (mean 1.3333) |
| `participation_contactprevprov` | int64 | 0.0% | 0.0 – 13.0 (mean 2.1733) |
| `finances_budgetprevprov` | int64 | 0.0% | 0.0 – 13.0 (mean 0.1733) |
| `finances_budgetprevprovhistorique` | int64 | 0.0% | 0.0 – 13.0 (mean 0.3067) |
| `finances_rapportaudit` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
| `finances_etatscomptableshistorique` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
| `finances_listebiens` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
| `finances_etatscomptables` | int64 | 0.0% | 0.0 – 13.0 (mean 0.1733) |
| `finances_marchespublicsfuturs` | int64 | 0.0% | 0.0 – 13.0 (mean 0.7467) |
| `finances_subventions` | int64 | 0.0% | 0.0 – 13.0 (mean 0.3067) |
| `gouvernance_organigramme` | int64 | 0.0% | 0.0 – 13.0 (mean 1.3733) |
| `gouvernance_planaction` | int64 | 0.0% | |
| `gouvernance_recrutement` | int64 | 0.0% | |
| `gouvernance_livreprocedure` | int64 | 0.0% | |
| `gouvernance_reglementinterieur` | int64 | 0.0% | |
| `participation` | int64 | 0.0% | |
| `finances` | int64 | 0.0% | |
| `gouvernance` | int64 | 0.0% | |
| `smiigbrut` | int64 | 0.0% | |
| `smiig` | float64 | 0.0% | |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-27 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `idregion` | 623.0 | 634.0 | 627.5467 | 628.0 |
| `idwilaya` | 19.0 | 30.0 | 23.5467 | 24.0 |
| `idprefprov` | 241.0 | 323.0 | 285.4667 | 286.0 |
| `annee` | 2023.0 | 2023.0 | 2023.0 | 2023.0 |
| `participation_contactaministration` | 0.0 | 0.0 | 0.0 | 0.0 |
| `participation_compositionconseil` | 0.0 | 10.0 | 1.7333 | 0.0 |
| `participation_agendaconseil` | 0.0 | 13.0 | 2.1333 | 0.0 |
| `participation_compositioncommissionsconseil` | 0.0 | 10.0 | 1.3333 | 0.0 |
| `participation_compositioninstancesconsultatives` | 0.0 | 10.0 | 0.4 | 0.0 |
| `participation_espaceconcertationenligne` | 0.0 | 10.0 | 1.3333 | 0.0 |
| `participation_contactprevprov` | 0.0 | 13.0 | 2.1733 | 0.0 |
| `finances_budgetprevprov` | 0.0 | 13.0 | 0.1733 | 0.0 |
| `finances_budgetprevprovhistorique` | 0.0 | 13.0 | 0.3067 | 0.0 |
| `finances_rapportaudit` | 0.0 | 0.0 | 0.0 | 0.0 |
| `finances_etatscomptableshistorique` | 0.0 | 0.0 | 0.0 | 0.0 |

---

## Curation

Raw data was downloaded from OpenAfrica via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

---

## Limitations

- Data originates from TAFRA and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- The following columns have >20% missing values and should be treated with caution in modelling: `url`.
- Refer to the [original HDX dataset page](https://open.africa/dataset/smiig-data-provinces-et-prefectures-2023) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{openafrica_africa_smiig_data_provinces_et_prefectures_2023,
title = {SMIIG Data provinces et préfectures Maroc 2023},
author = {TAFRA},
year = {2024},
url = {https://open.africa/dataset/smiig-data-provinces-et-prefectures-2023},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
```

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*