electricsheepafrica/africa-impact-of-cyclones

Mozambique: Impact of Cyclones

Publisher: OCHA Mozambique · Source: HDX · License: other-pd-nr · Updated: 2025-11-25

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Abstract

Impact of cyclones (people affected and injured by cyclones) in Mozambique from 2017 to 2025.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-11-25. Geographic scope: MOZ.

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

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Dataset Characteristics

Domain	Demographics and population
Unit of observation	Tabular records
Rows (total)	111
Columns	15 (11 numeric, 4 categorical, 0 datetime)
Train split	88 rows
Test split	22 rows
Geographic scope	MOZ
Publisher	OCHA Mozambique
HDX last updated	2025-11-25

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Variables

Identifier / Metadata — unnamed_0 (Nampula, Zambezia, Cabo Delgado), unnamed_1 (ADM2 PT, Chibabava, Cidade De Tete), unnamed_2 (range 0.0–1067630.0), unnamed_3 (range 0.0–512462.4), unnamed_4 (range 0.0–555167.6) and 6 others.

Other — 451910 (range 0.0–90749.0), 283493 (range 0.0–62003.0), 778036_3999999999 (range 0.0–76021.4), 1302815_4999999998 (range 0.0–90749.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-impact-of-cyclones")
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	object	0.0%	Nampula, Zambezia, Cabo Delgado
unnamed_1	object	0.0%	ADM2 PT, Chibabava, Cidade De Tete
unnamed_2	float64	0.9%	0.0 – 1067630.0 (mean 214115.9909)
unnamed_3	float64	0.9%	0.0 – 512462.4 (mean 102995.332)
unnamed_4	float64	0.9%	0.0 – 555167.6 (mean 111120.6589)
unnamed_5	float64	0.9%	0.0 – 608549.1 (mean 122046.1148)
unnamed_6	float64	0.9%	0.0 – 405699.4 (mean 81364.0765)
unnamed_7	float64	0.9%	0.0 – 53381.5 (mean 10705.7995)
unnamed_8	float64	0.9%	0.0 – 160144.5 (mean 32117.3986)
451910	float64	71.2%	0.0 – 90749.0 (mean 14122.1875)
283493	float64	79.3%	0.0 – 62003.0 (mean 12325.7826)
778036_3999999999	float64	60.4%	0.0 – 76021.4 (mean 17682.6455)
1302815_4999999998	float64	0.9%	0.0 – 90749.0 (mean 11843.7773)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-06

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Numeric Summary

Column	Min	Max	Mean	Median
unnamed_2	0.0	1067630.0	214115.9909	174587.5
unnamed_3	0.0	512462.4	102995.332	83220.96
unnamed_4	0.0	555167.6	111120.6589	94778.06
unnamed_5	0.0	608549.1	122046.1148	99514.875
unnamed_6	0.0	405699.4	81364.0765	66343.25
unnamed_7	0.0	53381.5	10705.7995	8729.375
unnamed_8	0.0	160144.5	32117.3986	26188.125
451910	0.0	90749.0	14122.1875	665.0
283493	0.0	62003.0	12325.7826	2320.0
778036_3999999999	0.0	76021.4	17682.6455	15565.05
1302815_4999999998	0.0	90749.0	11843.7773	57.0

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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. 11 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: 451910, 283493, 778036_3999999999.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_impact_of_cyclones,
  title     = {Mozambique: Impact of Cyclones},
  author    = {OCHA Mozambique},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/impact-of-cyclones},
  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.