electricsheepafrica/africa-mali-suivi-des-inondations

Mali: Suivi des Inondations

Publisher: OCHA Mali · Source: HDX · License: cc-by · Updated: 2025-05-05

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Abstract

Les données contiennent les impacts causés par les inondations et les fortes pluies au Mali.

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-05-05. Geographic scope: MLI.

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

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Dataset Characteristics

Domain	Climate and environment
Unit of observation	First-level administrative unit observations
Rows (total)	21
Columns	4 (1 numeric, 3 categorical, 0 datetime)
Train split	16 rows
Test split	4 rows
Geographic scope	MLI
Publisher	OCHA Mali
HDX last updated	2025-05-05

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Variables

Geographic — admin1_name (#adm1+name, Kayes, Nara).

Demographic — personnes_affectées (range 717.0–84458.0).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-18).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-mali-suivi-des-inondations")
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
admin1_name	object	0.0%	#adm1+name, Kayes, Nara
personnes_affectées	float64	4.8%	717.0 – 84458.0 (mean 18472.1)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-18

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Numeric Summary

Column	Min	Max	Mean	Median
personnes_affectées	717.0	84458.0	18472.1	7373.5

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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. 1 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 Mali 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_mali_suivi_des_inondations,
  title     = {Mali: Suivi des Inondations},
  author    = {OCHA Mali},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/mali-suivi-des-inondations},
  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.