electricsheepafrica/africa-mozambique-cyclone-kenneth-3w-4w

Mozambique Cyclone Kenneth 3W/4W

Publisher: OCHA Mozambique · Source: HDX · License: cc-by-igo · Updated: 2025-04-25

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Abstract

Mozambique 3W/4W (Response Tracking) for the the Cyclone Kenneth response.

Each row in this dataset represents tabular records. Temporal coverage is indicated by the when, unnamed_23 column(s). Geographic scope: MOZ.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Tabular records
Rows (total)	1,602
Columns	25 (2 numeric, 21 categorical, 2 datetime)
Train split	1,281 rows
Test split	320 rows
Geographic scope	MOZ
Publisher	OCHA Mozambique
HDX last updated	2025-04-25

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Variables

Identifier / Metadata — unnamed_0 (range 1.0–1599.0), unnamed_2 (Agências das Nações Unidas, ONGs internacionais, Governo), unnamed_3 (SEPPA, Ministerio de Saude, Direcções Provinciais da Educação e Desenvolvimento Humano), unnamed_4 (Governo, ONGs internacionais, ONGs nacionais), unnamed_6 (Food distribution in kind, Primary Health Care, Training and Technical Support) and 16 others.

Other — who (WFP, UNICEF, United Nations Children's Fund), what (Saúde, Segurança Alimentar, Proteção), where (Cabo Delgado, Nampula, Província), when.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-mozambique-cyclone-kenneth-3w-4w")
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
unnamed_0	float64	0.2%	1.0 – 1599.0 (mean 800.0)
who	object	0.0%	WFP, UNICEF, United Nations Children's Fund
unnamed_2	object	0.0%	Agências das Nações Unidas, ONGs internacionais, Governo
unnamed_3	object	18.8%	SEPPA, Ministerio de Saude, Direcções Provinciais da Educação e Desenvolvimento Humano
unnamed_4	object	19.1%	Governo, ONGs internacionais, ONGs nacionais
what	object	0.0%	Saúde, Segurança Alimentar, Proteção
unnamed_6	object	0.0%	Food distribution in kind, Primary Health Care, Training and Technical Support
unnamed_7	object	16.5%	Food ration, Distribution of NFIs, Equip classrooms with teaching materials (school in a box)
unnamed_8	object	54.4%	Cereals, beans, oil, HEB, # of water supply services
unnamed_9	object	0.1%	em espécie (in-kind), dinheiro em espécie, Delivery_modality
where	object	0.0%	Cabo Delgado, Nampula, Província
unnamed_11	object	0.0%
unnamed_12	object	1.6%
unnamed_13	object	1.6%
unnamed_14	object	46.9%
unnamed_15	object	46.9%
unnamed_16	object	59.1%
unnamed_20	float64	51.8%	0.0 – 193722.0 (mean 2500.2098)
unnamed_21	object	44.4%
when	datetime64[ns]	40.4%
unnamed_23	datetime64[ns]	55.1%
unnamed_24	object	17.3%
unnamed_25	object	77.4%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
unnamed_0	1.0	1599.0	800.0	800.0
unnamed_20	0.0	193722.0	2500.2098	400.0

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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. 4 column(s) with >80% missing values were removed: unnamed_17, unnamed_18, whom, unnamed_26. 4 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.
• The following columns have >20% missing values and should be treated with caution in modelling: unnamed_8, unnamed_14, unnamed_15, unnamed_16, unnamed_20, unnamed_21, when, unnamed_23....
• 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_cyclone_kenneth_3w_4w,
  title     = {Mozambique Cyclone Kenneth 3W/4W},
  author    = {OCHA Mozambique},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/mozambique-cyclone-kenneth-3w-4w},
  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.