electricsheepafrica/africa-mozambique-cyclone-1001053

Mozambique: Cyclone - Tropical storm - Mar 2024

Publisher: WFP Advanced Disaster Analysis & Mapping · Source: HDX · License: cc-by-sa · Updated: 2025-11-24

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Abstract

ADAM ID: 1001053_10 Cyclone (tropical storm) during the period Mar 03 2024-Mar 13 2024 in Mozambique, South Africa, Eswatini, Zimbabwe, Malawi. It impacted 0 people.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-11-24. 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)	168
Columns	8 (3 numeric, 5 categorical, 0 datetime)
Train split	134 rows
Test split	33 rows
Geographic scope	MOZ
Publisher	WFP Advanced Disaster Analysis & Mapping
HDX last updated	2025-11-24

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Variables

Demographic — pop_60_kmh (range 0.0–1923985.0), pop_90_kmh (range 0.0–472057.0).

Identifier / Metadata — unnamed_0 (range 0.0–187.0), adm0_name (Mozambique, Eswatini, South Africa), adm1_name (Manzini, Shiselweni, Hhohho), adm2_name (---, Dondo, Namaacha), esa_source (HDX) and 1 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-mozambique-cyclone-1001053")
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	int64	0.0%	0.0 – 187.0 (mean 91.8274)
adm0_name	object	0.0%	Mozambique, Eswatini, South Africa
adm1_name	object	0.0%	Manzini, Shiselweni, Hhohho
adm2_name	object	0.0%	---, Dondo, Namaacha
pop_60_kmh	int64	0.0%	0.0 – 1923985.0 (mean 160311.0536)
pop_90_kmh	int64	0.0%	0.0 – 472057.0 (mean 8122.5179)
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_0	0.0	187.0	91.8274	91.5
pop_60_kmh	0.0	1923985.0	160311.0536	40792.0
pop_90_kmh	0.0	472057.0	8122.5179	0.0

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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 WFP Advanced Disaster Analysis & Mapping 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_cyclone_1001053,
  title     = {Mozambique: Cyclone - Tropical storm - Mar 2024},
  author    = {WFP Advanced Disaster Analysis & Mapping},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/mozambique-cyclone-1001053},
  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.