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
- education
- education-facilities-schools
- sen
pretty_name: Répartition des établissements scolaires au Sénégal en 2016
dataset_info:
splits:
- name: train
num_examples: 441
- name: test
num_examples: 110
Répartition des établissements scolaires au Sénégal en 2016
Publisher: Agence Nationale de la Statistique et de la Démographie du Sénégal · Source: HDX · License: cc-by · Updated: 2024-09-13
Abstract
Ce jeu de données concerne le nombre d'établissements élémentaire, maternel, moyen et secondaire du Sénégal désagrégé jusqu'au niveau commune rural et commune d'arrondissement.
Each row in this dataset represents tabular records. Data was last updated on HDX on 2024-09-13. Geographic scope: SEN.
Curated into ML-ready Parquet format by Electric Sheep Africa.
Dataset Characteristics
| Domain | Education |
| Unit of observation | Tabular records |
| Rows (total) | 552 |
| Columns | 18 (11 numeric, 7 categorical, 0 datetime) |
| Train split | 441 rows |
| Test split | 110 rows |
| Geographic scope | SEN |
| Publisher | Agence Nationale de la Statistique et de la Démographie du Sénégal |
| HDX last updated | 2024-09-13 |
Variables
Geographic — moyen_secondaire (range 0.0–43.0).
Demographic — n_menage (range 7.0–80404.0).
Outcome / Measurement — total_général (range 0.0–193.0).
Identifier / Metadata — n_individ (range 93.0–727266.0), esa_source (HDX), esa_processed (2026-04-18).
Other — reg (LOUGA, DAKAR, THIES), dept (PODOR, DAKAR, LINGUERE), cav (KAEL, DAROU MOUSTY, PIKINE DAGOUDANE), cod_reg (range 1.0–14.0), cod_dept (range 1.0–4.0) and 7 others.
Quick Start
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-repartition-des-etablissements-scolaires-au-senegal")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()
print(train.shape)
train.head()
Schema
| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
reg |
object | 0.0% | LOUGA, DAKAR, THIES |
dept |
object | 0.0% | PODOR, DAKAR, LINGUERE |
cav |
object | 0.0% | KAEL, DAROU MOUSTY, PIKINE DAGOUDANE |
cod_reg |
int64 | 0.0% | 1.0 – 14.0 (mean 7.1268) |
cod_dept |
int64 | 0.0% | 1.0 – 4.0 (mean 2.0036) |
cod_cav |
int64 | 0.0% | 101.0 – 301.0 (mean 188.8116) |
cod_ccrca |
int64 | 0.0% | 0.0 – 44.0 (mean 3.5761) |
cod_entite |
int64 | 0.0% | 1130111.0 – 14320304.0 (mean 7346058.6486) |
ccrca |
object | 0.0% | PATAR, VELINGARA, DINGUIRAYE |
commune |
object | 0.0% | Goree, Passy, Patar |
elementaire |
float64 | 0.2% | 0.0 – 100.0 (mean 16.7495) |
maternelle |
float64 | 17.0% | 0.0 – 61.0 (mean 6.0197) |
moyen_secondaire |
float64 | 9.4% | 0.0 – 43.0 (mean 3.814) |
total_général |
int64 | 0.0% | 0.0 – 193.0 (mean 25.1685) |
n_menage |
int64 | 0.0% | 7.0 – 80404.0 (mean 2772.6087) |
n_individ |
int64 | 0.0% | 93.0 – 727266.0 (mean 24027.317) |
esa_source |
object | 0.0% | HDX |
esa_processed |
object | 0.0% | 2026-04-18 |
Numeric Summary
| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
cod_reg |
1.0 | 14.0 | 7.1268 | 7.0 |
cod_dept |
1.0 | 4.0 | 2.0036 | 2.0 |
cod_cav |
101.0 | 301.0 | 188.8116 | 202.0 |
cod_ccrca |
0.0 | 44.0 | 3.5761 | 2.0 |
cod_entite |
1130111.0 | 14320304.0 | 7346058.6486 | 7230116.0 |
elementaire |
0.0 | 100.0 | 16.7495 | 15.0 |
maternelle |
0.0 | 61.0 | 6.0197 | 3.0 |
moyen_secondaire |
0.0 | 43.0 | 3.814 | 2.0 |
total_général |
0.0 | 193.0 | 25.1685 | 20.0 |
n_menage |
7.0 | 80404.0 | 2772.6087 | 1470.0 |
n_individ |
93.0 | 727266.0 | 24027.317 | 14859.0 |
Curation
Raw data was downloaded from HDX 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 Agence Nationale de la Statistique et de la Démographie du Sénégal and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.
Citation
@dataset{hdx_africa_repartition_des_etablissements_scolaires_au_senegal,
title = {Répartition des établissements scolaires au Sénégal en 2016},
author = {Agence Nationale de la Statistique et de la Démographie du Sénégal},
year = {2024},
url = {https://data.humdata.org/dataset/repartition-des-etablissements-scolaires-au-senegal},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.