electricsheepafrica/africa-somalia-floods

Somalia : Floods

Publisher: OCHA Somalia · Source: HDX · License: cc-by · Updated: 2025-09-24

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Abstract

This dataset is from preliminary reports by the Ministry of Humanitarian Affairs and Disaster Management, South West State and humanitarian partners.

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2025-09-24. Geographic scope: SOM.

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

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Dataset Characteristics

Domain	Climate and environment
Unit of observation	Subnational administrative unit observations
Rows (total)	74
Columns	20 (11 numeric, 9 categorical, 0 datetime)
Train split	59 rows
Test split	14 rows
Geographic scope	SOM
Publisher	OCHA Somalia
HDX last updated	2025-09-24

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Variables

Geographic — state (South West , Jubaland, Puntland), region (Lower Shabelle, Gedo, Bari), region_pcodes (SO23, SO26, SO16), district (Afgooye, Caluula, Cadale), district_pcodes (SO2302, SO1603, SO2104) and 8 others.

Outcome / Measurement — people_affected (0, 600 , 1,200 ), shelters_affected (range 0.0–2000.0), farmland_affected (range 0.0–20733.0).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-08).

Other — people_relocated (range 0.0–120000.0), dead_livestock (range 0.0–1000.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-somalia-floods")
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
state	object	0.0%	South West , Jubaland, Puntland
region	object	0.0%	Lower Shabelle, Gedo, Bari
region_pcodes	object	0.0%	SO23, SO26, SO16
district	object	0.0%	Afgooye, Caluula, Cadale
district_pcodes	object	0.0%	SO2302, SO1603, SO2104
population	object	0.0%	505,857 , 86,601 , 75,977
people_affected	object	0.0%	0, 600 , 1,200
people_displaced	int64	0.0%	0.0 – 220460.0 (mean 12144.0541)
people_relocated	int64	0.0%	0.0 – 120000.0 (mean 1697.2973)
people_killed_by_floods	int64	0.0%	0.0 – 20.0 (mean 1.5946)
shelters_affected	int64	0.0%	0.0 – 2000.0 (mean 63.2297)
shelters_destroyed	int64	0.0%	0.0 – 6593.0 (mean 286.6081)
latrines_destroyed	int64	0.0%	0.0 – 6587.0 (mean 214.1622)
water_point_destroyed	int64	0.0%	0.0 – 669.0 (mean 20.1081)
farmland_affected	int64	0.0%	0.0 – 20733.0 (mean 561.5676)
brides_destroyed	int64	0.0%	0.0 – 2.0 (mean 0.0405)
dead_livestock	int64	0.0%	0.0 – 1000.0 (mean 13.5135)
ofschools_destroyed	int64	0.0%	0.0 – 72.0 (mean 3.027)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-08

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Numeric Summary

Column	Min	Max	Mean	Median
people_displaced	0.0	220460.0	12144.0541	12.0
people_relocated	0.0	120000.0	1697.2973	0.0
people_killed_by_floods	0.0	20.0	1.5946	0.0
shelters_affected	0.0	2000.0	63.2297	0.0
shelters_destroyed	0.0	6593.0	286.6081	0.0
latrines_destroyed	0.0	6587.0	214.1622	0.0
water_point_destroyed	0.0	669.0	20.1081	0.0
farmland_affected	0.0	20733.0	561.5676	0.0
brides_destroyed	0.0	2.0	0.0405	0.0
dead_livestock	0.0	1000.0	13.5135	0.0
ofschools_destroyed	0.0	72.0	3.027	0.0

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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. 9 column(s) with >80% missing values were removed: roads_destroyed, people_reached_fsc, people_reached_nutrition, people_reached_wash, people_reached_health, people_reached_education.... 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 Somalia 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_somalia_floods,
  title     = {Somalia : Floods},
  author    = {OCHA Somalia},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/somalia-floods},
  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.