electricsheepafrica/africa-burkina-faso-suivi-des-inondations

Burkina Faso: Suivi des Inondations

Publisher: OCHA Burkina Faso · Source: HDX · License: cc-by · Updated: 2024-12-02

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Abstract

Ces données du CONASUR contiennent les informations sur l'impacts des inondations au Burkina Faso en 2021, 2022, 2023 et 2024. Il contient le numbre de personnes ou foyers affectés.

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2024-12-02. Geographic scope: BFA.

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)	39
Columns	5 (2 numeric, 3 categorical, 0 datetime)
Train split	31 rows
Test split	7 rows
Geographic scope	BFA
Publisher	OCHA Burkina Faso
HDX last updated	2024-12-02

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Variables

Geographic — region (BOUCLE DU MOUHOUN, CENTRE-EST, CENTRE-NORD).

Demographic — personnes_affectées (range 0.0–13309.0).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-17).

Other — annee (range 2021.0–2024.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-burkina-faso-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
annee	float64	2.6%	2021.0 – 2024.0 (mean 2022.4474)
region	object	0.0%	BOUCLE DU MOUHOUN, CENTRE-EST, CENTRE-NORD
personnes_affectées	float64	2.6%	0.0 – 13309.0 (mean 1914.1579)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-17

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Numeric Summary

Column	Min	Max	Mean	Median
annee	2021.0	2024.0	2022.4474	2022.0
personnes_affectées	0.0	13309.0	1914.1579	412.0

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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. 2 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 Burkina Faso 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_burkina_faso_suivi_des_inondations,
  title     = {Burkina Faso: Suivi des Inondations},
  author    = {OCHA Burkina Faso},
  year      = {2024},
  url       = {https://data.humdata.org/dataset/burkina-faso-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.