electricsheepafrica/africa-mozambique-school-data

Mozambique: Schools

Publisher: OCHA Mozambique · Source: HDX · License: cc-by · Updated: 2025-05-05

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Abstract

Mozambique school data at lowest administrative level available.

Each row in this dataset represents geolocated point observations. Data was last updated on HDX on 2025-05-05. Geographic scope: MOZ.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Education
Unit of observation	Geolocated point observations
Rows (total)	13,010
Columns	27 (6 numeric, 21 categorical, 0 datetime)
Train split	10,408 rows
Test split	2,602 rows
Geographic scope	MOZ
Publisher	OCHA Mozambique
HDX last updated	2025-05-05

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Variables

Geographic — latrina, totallatri (range 0.0–58.0), longitude (range 30.2394–40.8219), latitude (range -26.8445–-10.5675).

Outcome / Measurement — totalurino (range 0.0–30.0), totalcb (range 0.0–31.0).

Identifier / Metadata — localidade (Cidade de Mocuba, Beira, Sede), esa_source, esa_processed.

Other — provincia (Zambézia, Nampula, Tete), distrito (Milange, Gurué, Mocuba), posto (Milange sede, Nauela, Gurué Sede), povoado (Nenhum, Cidade de Nampula, Cidade de Mocuba), cod_escola (range 8104.0–620414.0) and 13 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-mozambique-school-data")
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
provincia	object	0.0%	Zambézia, Nampula, Tete
distrito	object	0.0%	Milange, Gurué, Mocuba
posto	object	0.0%	Milange sede, Nauela, Gurué Sede
localidade	object	0.0%	Cidade de Mocuba, Beira, Sede
povoado	object	0.1%	Nenhum, Cidade de Nampula, Cidade de Mocuba
cod_escola	float64	13.9%	8104.0 – 620414.0 (mean 67206.6694)
nomeesco_1	object	0.0%	Escola Primária Completa Eduardo Mondlane, Escola Primária Samora Machel, Escola Primária Completa 25 de Junho
zip	object	0.5%	Nenhum, Quême, Nihesiue
agua	object	0.0%	Não, Sim
tipofonte	object	0.0%	Sim, Nenhuma Fonte, Furo com bomba
qualiagua	object	0.0%	Nenhuma, Potável, Não Potável
energia	object	0.0%
tipoenergi	object	0.0%
latrina	object	0.0%
totallatri	float64	0.0%	0.0 – 58.0 (mean 2.2881)
estado_de_conservação3	object	0.0%
urinois	object	0.0%
totalurino	float64	0.0%	0.0 – 30.0 (mean 1.1347)
urinoisfun	object	0.0%
estado_de_conservação4	object	0.0%
casabanho	object	0.0%
totalcb	float64	0.0%	0.0 – 31.0 (mean 1.1361)
cbfunci	object	0.0%
longitude	float64	0.1%	30.2394 – 40.8219 (mean 36.1759)
latitude	float64	0.1%	-26.8445 – -10.5675 (mean -17.6031)
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
cod_escola	8104.0	620414.0	67206.6694	70135.0
totallatri	0.0	58.0	2.2881	2.0
totalurino	0.0	30.0	1.1347	0.0
totalcb	0.0	31.0	1.1361	0.0
longitude	30.2394	40.8219	36.1759	35.9839
latitude	-26.8445	-10.5675	-17.6031	-16.3178

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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. 12 exact duplicate rows were removed. 2 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). 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 OCHA Mozambique 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_mozambique_school_data,
  title     = {Mozambique: Schools},
  author    = {OCHA Mozambique},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/mozambique-school-data},
  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.