electricsheepafrica/africa-somalia-flood-hazard-data-for-disaster-risk-assessment-selected-co-9ec1f5bf

Flood Hazard Data for Disaster Risk Assessment Selected Co | Africa (ETH Zürich - Weather and Climate Risks)

295,411 rows - 1 Africa country/area - 2000-2018 - source table - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

TL;DR

This dataset contains 295,411 rows from ETH Zürich - Weather and Climate Risks, covering Flood Hazard Data for Disaster Risk Assessment Selected Co. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Official statistics datasets help analysts inspect public data as published by governments, national statistical systems, and regional data portals.

Source-provided context: Gridded (200mx200m) flood extent for Nigeria at with admin1 name column - non flood grid points ommitted

How To Read This Dataset

• —One row means: one source record from the original tabular resource, with Electric Sheep Africa provenance columns added where available.
• —Primary geography column: country_iso3.
• —Best time column: not detected.
• —Time coverage basis: source metadata.
• —Recommended join keys: country_iso3 where available plus source-specific keys.

Coverage

Geographic Coverage

Top areas shown below, sorted by row count when available:

Indicators, Variables, Or Resource Contents

• —This repo preserves one source tabular resource with its usable columns kept together.

Schema

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-somalia-flood-hazard-data-for-disaster-risk-assessment-selected-co-9ec1f5bf")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

print(df.info())
print(df.head())

Filter By Geography

if "country_iso3" in df.columns:
    sample = df[df["country_iso3"] == "SOM"]

Time-Series Pattern

if "value" in df.columns and "year" in df.columns:
    trend = df.sort_values("year")

Pivot For Analysis

if {"indicator_id", "year", "value"}.issubset(df.columns):
    matrix = df.pivot_table(index="year", columns="indicator_id", values="value")
    print(matrix.tail())

Data Quality Notes

• —No canonical year/date column was detected in the packaged table; use source metadata and domain context for temporal interpretation.
• —Missing values are preserved rather than silently imputed.
• —Column names are standardized for machine use; source meanings are preserved where known.
• —Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use.

Source And Provenance

• —Source: ETH Zürich - Weather and Climate Risks
• —Publisher: ETH Zürich - Weather and Climate Risks
• —Portal: https://data.humdata.org
• —Resource: nigeria-admin1-flood.csv
• —License: CC BY 4.0
• —Retrieved/generated: 2026-08-10T16:03:50Z
• —Hugging Face repo: electricsheepafrica/africa-somalia-flood-hazard-data-for-disaster-risk-assessment-selected-co-9ec1f5bf

Transformations Applied

• —Converted the source table to Parquet for efficient analytics and ML workflows.
• —Added or preserved source provenance columns where available.
• —Standardized README metadata, dataset loading configuration, schema documentation, and citation format.
• —Preserved source-reported values without analytical imputation.

Suggested Analyses

• —Profile the distribution of values
• —Compare categories or geographies
• —Join with complementary public datasets
• —Check missingness before modeling
• —Use country_iso3 as the safest geography join key when present

Citation

@misc{electric_sheep_africa_africa_somalia_flood_hazard_data_for_disaster_risk_assessment_selected_co_9ec1f5_2018,
  title        = {Flood Hazard Data for Disaster Risk Assessment Selected Co | Africa (ETH Zürich - Weather and Climate Risks)},
  author       = {ETH Zürich - Weather and Climate Risks},
  year         = {2018},
  url          = {https://data.humdata.org/dataset/climada-flood-dataset},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-somalia-flood-hazard-data-for-disaster-risk-assessment-selected-co-9ec1f5bf}}
}

License

Released under CC BY 4.0.

Original data is published by ETH Zürich - Weather and Climate Risks. Electric Sheep Africa engineering standardizes the data for discovery, loading, and analysis on Hugging Face. Cite both the original source and this ML-ready dataset when used.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.

Provenance: README standardized 2026-08-12 by the Electric Sheep Africa README system. Source URL: https://data.humdata.org/dataset/climada-flood-dataset