electricsheepafrica/africa-chad-flood-hazard-data-for-disaster-risk-assessment-selected-co-0de3e376

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

1,990 rows - 1 Africa country/area - 2000-2018 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 1,990 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 Congo 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

Dimension	Value
Rows	1,990
Countries/areas	1
First period	2000
Last period	2018
Indicators	0
Columns	21
Source format	CSV

Geographic Coverage

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

Area	Rows	First year	Last year	Name
TCD	1,990	2000	2018	Chad

Indicators, Variables, Or Resource Contents

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

Schema

Column	Type	Description	Example
source_record_id	string	Stable row identifier assigned during Electric Sheep Africa engineering.	3cabe801-3563-4765-9476-3f3f1c151318:0
country_iso3	dictionary<values=string, indices=int8, ordered=0>	ISO3 country or area code.	TCD
country_name	dictionary<values=string, indices=int8, ordered=0>	Country or area name.	Chad
country_name_2	string	Source column from the original resource.	#country
admin1_name	string	Source column from the original resource.	#adm1+name
latitude	double	Source column from the original resource.	``
longitude	double	Source column from the original resource.	``
aggregation	string	Source column from the original resource.	``
indicator	string	Source column from the original resource.	#indicator+name
value	double	Numeric observation value.	``
source_period_start_year	int64	Start year inferred from source metadata.	2000
source_period_end_year	int64	End year inferred from source metadata.	2018
source_period_label	dictionary<values=string, indices=int8, ordered=0>	Source column from the original resource.	2000-2018
source_provider	dictionary<values=string, indices=int8, ordered=0>	Publishing organization.	ETH Zürich - Weather and Climate Risks
source_dataset	dictionary<values=string, indices=int8, ordered=0>	Source dataset or package title.	Flood: Hazard Data for Disaster Risk Assessment (selected countries)
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	congo-admin1-flood.csv
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	46c703fe-7ba1-484b-a38c-8c53f0cf00c4
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	3cabe801-3563-4765-9476-3f3f1c151318
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://data.humdata.org/dataset/46c703fe-7ba1-484b-a38c-8c53f0cf00c4...
license_id	dictionary<values=string, indices=int8, ordered=0>	Source license identifier.	cc-by
retrieved_at	dictionary<values=string, indices=int8, ordered=0>	UTC source retrieval timestamp from the Electric Sheep Africa pipeline.	2026-08-10T22:51:43Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-chad-flood-hazard-data-for-disaster-risk-assessment-selected-co-0de3e376")
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"] == "TCD"]

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: congo-admin1-flood.csv
• License: CC BY 4.0
• Retrieved/generated: 2026-08-11T00:14:40Z
• Hugging Face repo: electricsheepafrica/africa-chad-flood-hazard-data-for-disaster-risk-assessment-selected-co-0de3e376

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_chad_flood_hazard_data_for_disaster_risk_assessment_selected_co_0de3e376_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-chad-flood-hazard-data-for-disaster-risk-assessment-selected-co-0de3e376}}
}

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.

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Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://data.humdata.org/dataset/climada-flood-dataset