electricsheepafrica/africa-south-sudan-flood-data

South Sudan: Flood Data

Publisher: OCHA South Sudan · Source: HDX · License: other-pd-nr · Updated: 2025-12-31

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Abstract

Reported number of flood-affected people by county and state in South Sudan.

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2025-12-31. Geographic scope: SSD.

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)	79
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	63 rows
Test split	15 rows
Geographic scope	SSD
Publisher	OCHA South Sudan
HDX last updated	2025-12-31

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Variables

Geographic — admin1 (Upper Nile, Jonglei, Western Equatoria), admin1pcode (SS07, SS03, SS10), admin2 (Abyei Administrative Area, Longochuk, Panyikang), admin2_pcode (SS0001, SS0703, SS0710), peaple_displaced (range 0.0–89106.0).

Outcome / Measurement — people_affected (range 1000.0–146851.0).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-05).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-south-sudan-flood-data")
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	object	0.0%	Upper Nile, Jonglei, Western Equatoria
admin1pcode	object	0.0%	SS07, SS03, SS10
admin2	object	0.0%	Abyei Administrative Area, Longochuk, Panyikang
admin2_pcode	object	0.0%	SS0001, SS0703, SS0710
people_affected	float64	51.9%	1000.0 – 146851.0 (mean 35522.7368)
peaple_displaced	float64	60.8%	0.0 – 89106.0 (mean 12116.7742)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-05

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Numeric Summary

Column	Min	Max	Mean	Median
people_affected	1000.0	146851.0	35522.7368	18096.0
peaple_displaced	0.0	89106.0	12116.7742	2100.0

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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. 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 South Sudan and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• The following columns have >20% missing values and should be treated with caution in modelling: people_affected, peaple_displaced.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_south_sudan_flood_data,
  title     = {South Sudan: Flood Data},
  author    = {OCHA South Sudan},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/south-sudan-flood-data},
  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.