electricsheepafrica/africa-mauritius-cyclone-1001052

Mauritius: Cyclone - Tropical storm - Feb 2024

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

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Abstract

ADAM ID: 1001052_19 Cyclone (tropical storm) during the period Feb 19 2024-Feb 23 2024 in Miscellaneous (French) Indian Ocean Islands, Mauritius. It impacted 0 people.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-11-24. Geographic scope: MUS.

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)	12
Columns	8 (3 numeric, 5 categorical, 0 datetime)
Train split	9 rows
Test split	2 rows
Geographic scope	MUS
Publisher	WFP Advanced Disaster Analysis & Mapping
HDX last updated	2025-11-24

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Variables

Demographic — pop_60_kmh (range 0.0–550358.0), pop_90_kmh (range 0.0–357295.0).

Identifier / Metadata — unnamed_0 (range 0.0–12.0), adm0_name (Mauritius, Réunion), adm1_name (Black River, Flacq, Grand Port), adm2_name (---), esa_source (HDX) and 1 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-mauritius-cyclone-1001052")
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 – 12.0 (mean 5.6667)
adm0_name	object	0.0%	Mauritius, Réunion
adm1_name	object	0.0%	Black River, Flacq, Grand Port
adm2_name	object	0.0%	---
pop_60_kmh	int64	0.0%	0.0 – 550358.0 (mean 72645.9167)
pop_90_kmh	int64	0.0%	0.0 – 357295.0 (mean 99521.75)
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	12.0	5.6667	5.5
pop_60_kmh	0.0	550358.0	72645.9167	0.0
pop_90_kmh	0.0	357295.0	99521.75	96865.0

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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_mauritius_cyclone_1001052,
  title     = {Mauritius: Cyclone - Tropical storm - Feb 2024},
  author    = {WFP Advanced Disaster Analysis & Mapping},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/mauritius-cyclone-1001052},
  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.