electricsheepafrica/africa-cyclone-dineo-windspeeds

Cyclone Dineo - Windspeeds and rainfall

Publisher: Netherlands Red Cross - 510 · Source: HDX · License: cc-by · Updated: 2024-06-06

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Abstract

Dataset contains windspeeds in miles per hour.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2024-06-06. Geographic scope: MOZ.

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

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Dataset Characteristics

Domain	Climate and environment
Unit of observation	Tabular records
Rows (total)	406
Columns	5 (2 numeric, 3 categorical, 0 datetime)
Train split	324 rows
Test split	81 rows
Geographic scope	MOZ
Publisher	Netherlands Red Cross - 510
HDX last updated	2024-06-06

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Variables

Outcome / Measurement — rainfallme (range 0.0–396.9514).

Identifier / Metadata — unnamed_0 (range 0.0–405.0), m_code (MZ001202001, MZ008108277, MZ008109286), esa_source (HDX), esa_processed (2026-04-06).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-cyclone-dineo-windspeeds")
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 – 405.0 (mean 202.5)
m_code	object	0.0%	MZ001202001, MZ008108277, MZ008109286
rainfallme	float64	0.0%	0.0 – 396.9514 (mean 44.2003)
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	405.0	202.5	202.5
rainfallme	0.0	396.9514	44.2003	24.9235

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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 Netherlands Red Cross - 510 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_cyclone_dineo_windspeeds,
  title     = {Cyclone Dineo - Windspeeds and rainfall},
  author    = {Netherlands Red Cross - 510},
  year      = {2024},
  url       = {https://data.humdata.org/dataset/cyclone-dineo-windspeeds},
  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.