electricsheepafrica/africa-niger-situation-des-inondations

Niger : Situation des inondations

Publisher: OCHA Niger · Source: HDX · License: cc-by · Updated: 2025-11-14

---

Abstract

Ce jeu donne données donne la situation des inondations au Niger.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2025-11-14. Geographic scope: NER.

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

---

Dataset Characteristics

Domain	Climate and environment
Unit of observation	Country-level aggregates
Rows (total)	62
Columns	32 (25 numeric, 7 categorical, 0 datetime)
Train split	49 rows
Test split	12 rows
Geographic scope	NER
Publisher	OCHA Niger
HDX last updated	2025-11-14

---

Variables

Geographic — iso3 (NER), maisons_effondrées (range 0.0–8023.0), pertes_en_vie_humaine_par_noyade (range 0.0–5.0), perte_d_animaux_par_petits_ruminants (range 0.0–144.0), perte_d_animaux_par_gros_ruminant (range 0.0–7.0) and 1 others.

Demographic — ménages_sinistrés (range 1.0–8474.0), personnes_sinistrées (range 0.0–88107.0), forages_endommagés (range 0.0–0.0).

Outcome / Measurement — cases_endommagées (range 0.0–571.0).

Identifier / Metadata — pcode1 (NE006, NE005, NE007), pcode2 (NER005001, NER007008, NER001005), esa_source (HDX), esa_processed (2026-04-04).

Other — région (Tillabéri, Tahoua, Zinder), département (Abalak, Takeita, Ingall), pertes_en_vie_humaine_par_effond (range 0.0–14.0), pertes_en_vie_humaine_par_foudre (range 0.0–2.0), aires_de_cultures_inondées_ha (range 0.0–2053.0) and 13 others.

---

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-niger-situation-des-inondations")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

---

Schema

Column	Type	Null %	Range / Sample Values
iso3	object	0.0%	NER
pcode1	object	0.0%	NE006, NE005, NE007
région	object	0.0%	Tillabéri, Tahoua, Zinder
pcode2	object	0.0%	NER005001, NER007008, NER001005
département	object	0.0%	Abalak, Takeita, Ingall
ménages_sinistrés	int64	0.0%	1.0 – 8474.0 (mean 986.7742)
personnes_sinistrées	int64	0.0%	0.0 – 88107.0 (mean 7640.871)
maisons_effondrées	int64	0.0%	0.0 – 8023.0 (mean 749.1935)
cases_endommagées	int64	0.0%	0.0 – 571.0 (mean 31.8387)
pertes_en_vie_humaine_par_noyade	int64	0.0%	0.0 – 5.0 (mean 0.3387)
pertes_en_vie_humaine_par_effond	int64	0.0%	0.0 – 14.0 (mean 1.5806)
pertes_en_vie_humaine_par_foudre	int64	0.0%	0.0 – 2.0 (mean 0.0645)
perte_d_animaux_par_petits_ruminants	int64	0.0%	0.0 – 144.0 (mean 8.2097)
perte_d_animaux_par_gros_ruminant	int64	0.0%	0.0 – 7.0 (mean 0.2419)
aires_de_cultures_inondées_ha	float64	0.0%	0.0 – 2053.0 (mean 143.1763)
latrines	int64	0.0%	0.0 – 77.0 (mean 3.0)
mur	int64	0.0%	0.0 – 1243.0 (mean 26.3065)
hangars	int64	0.0%	0.0 – 8.0 (mean 0.5161)
magasins	int64	0.0%	0.0 – 23.0 (mean 0.6129)
boutiques	int64	0.0%	0.0 – 7.0 (mean 0.2258)
greniers	int64	0.0%	0.0 – 25.0 (mean 0.4677)
vivre_tonne	int64	0.0%	0.0 – 0.0 (mean 0.0)
puits_effondrés	int64	0.0%	0.0 – 4.0 (mean 0.0968)
puits_ensevelis	int64	0.0%	0.0 – 14.0 (mean 0.2903)
forages_endommagés	int64	0.0%	0.0 – 0.0 (mean 0.0)
lieu_de_culte	int64	0.0%
centre_de_santé	int64	0.0%
classe	int64	0.0%
route	float64	1.6%
pont_endommagé	int64	0.0%
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-04

---

Numeric Summary

Column	Min	Max	Mean	Median
ménages_sinistrés	1.0	8474.0	986.7742	513.5
personnes_sinistrées	0.0	88107.0	7640.871	3801.0
maisons_effondrées	0.0	8023.0	749.1935	352.0
cases_endommagées	0.0	571.0	31.8387	0.0
pertes_en_vie_humaine_par_noyade	0.0	5.0	0.3387	0.0
pertes_en_vie_humaine_par_effond	0.0	14.0	1.5806	0.0
pertes_en_vie_humaine_par_foudre	0.0	2.0	0.0645	0.0
perte_d_animaux_par_petits_ruminants	0.0	144.0	8.2097	0.0
perte_d_animaux_par_gros_ruminant	0.0	7.0	0.2419	0.0
aires_de_cultures_inondées_ha	0.0	2053.0	143.1763	0.0
latrines	0.0	77.0	3.0	0.0
mur	0.0	1243.0	26.3065	0.0
hangars	0.0	8.0	0.5161	0.0
magasins	0.0	23.0	0.6129	0.0
boutiques	0.0	7.0	0.2258	0.0

---

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.

---

Limitations

• Data originates from OCHA Niger 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.

---

Citation

@dataset{hdx_africa_niger_situation_des_inondations,
  title     = {Niger : Situation des inondations},
  author    = {OCHA Niger},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/niger-situation-des-inondations},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.