electricsheepafrica/africa-repartition-des-etablissements-scolaires-au-senegal

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

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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.

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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

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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.

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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()

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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

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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

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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.

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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.

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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)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.