Datasetpublic

electricsheepafrica/africa-niger-flood-hazard-data-for-disaster-risk-assessment-selected-co-334c4a27

Flood: Hazard Data for Disaster Risk Assessment (selected countries) | Africa (Niger official open data) 8,883 rows - 1 Africa country - 2000-2018 - Repackaged by Electric Sheep Africa TL;DR This dataset packages one official CSV resource from Niger as ML-ready Parquet. The source file is the provenance boundary; all usable indicators or tabular columns from the resource stay together in this repo. About the source Source: Flood: Hazard Data… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-niger-flood-hazard-data-for-disaster-risk-assessment-selected-co-334c4a27.

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Dataset Card

Flood: Hazard Data for Disaster Risk Assessment (selected countries) | Africa (Niger official open data)

8,883 rows - 1 Africa country - 2000-2018 - Repackaged by Electric Sheep Africa

TL;DR

This dataset packages one official CSV resource from Niger as ML-ready Parquet. The source file is the provenance boundary; all usable indicators or tabular columns from the resource stay together in this repo.

About the source

—Source: Flood: Hazard Data for Disaster Risk Assessment (selected countries)
—Publisher: ETH Zürich - Weather and Climate Risks
—Resource: admin1-timeseries-summaries-flood.csv
—Format: CSV
—License: CC BY 4.0
—Packaging mode: indicator_long

Geographic coverage

1 Africa country:

Country	Rows	First year	Last year	Name
`NER`	8,883	2000	2018	`Niger`

Indicators or Resource Contents

—flood-hazard-data-for-disaster-risk-assessment-selected-countries-latitu-381551aa - Flood: Hazard Data for Disaster Risk Assessment (selected countries) - latitude
—flood-hazard-data-for-disaster-risk-assessment-selected-countries-longit-6b755a49 - Flood: Hazard Data for Disaster Risk Assessment (selected countries) - longitude
—flood-hazard-data-for-disaster-risk-assessment-selected-countries-value-f653f0aa - Flood: Hazard Data for Disaster Risk Assessment (selected countries) - value

Schema

Column	Type	Description	Example
`indicator_id`	`string`	Stable indicator identifier.	`flood-hazard-data-for-disaster-risk-assessment-selected-countries-latitu`
`indicator_name`	`string`	Human-readable indicator name.	`Flood: Hazard Data for Disaster Risk Assessment (selected countries) - l`
`country_iso3`	`string`	ISO3 country code.	`NER`
`country_name`	`string`	Country name.	`Niger`
`year`	`Int64`	Observation year.	`2013`
`value`	`float64`	Numeric observation value.	`34.6418`
`unit`	`string`	Measurement unit, when available.	`source_units_unspecified`
`dimension_country_name`	`string`	Source dimension.	`Afghanistan`
`dimension_admin1_name`	`string`	Source dimension.	`Kabul`
`dimension_admin2_name`	`string`	Source dimension.	``
`dimension_aggregation`	`string`	Source dimension.	`sum`
`dimension_indicator`	`string`	Source dimension.	`flood.date`
`source_period_start_year`	`Int64`	First year inferred from source resource metadata.	`2000`
`source_period_end_year`	`Int64`	Last year inferred from source resource metadata.	`2018`
`source_period_label`	`category`	Human-readable period inferred from source resource metadata.	`2000-2018`
`source_provider`	`category`	Publishing organization.	`ETH Zürich - Weather and Climate Risks`
`source_dataset`	`category`	Source package title.	`Flood: Hazard Data for Disaster Risk Assessment (selected countries)`
`source_resource`	`category`	Source resource title.	`admin1-timeseries-summaries-flood.csv`
`source_package_id`	`category`	CKAN package UUID.	`46c703fe-7ba1-484b-a38c-8c53f0cf00c4`
`source_resource_id`	`category`	CKAN resource UUID.	`5b49391f-4251-4e5b-977a-ee1ea49e2034`
`source_url`	`category`	Original source resource URL.	`https://data.humdata.org/dataset/46c703fe-7ba1-484b-a38c-8c53f0cf00c4/re`
`license_id`	`category`	Source license identifier.	`cc-by`
`retrieved_at`	`category`	UTC retrieval timestamp.	`2026-08-14T00:35:21Z`

Usage

python

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-niger-flood-hazard-data-for-disaster-risk-assessment-selected-co-334c4a27")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

python

sample_country = df[df["country_iso3"] == "NER"]

Work with indicators

python

if "indicator_id" in df.columns:
    print(df["indicator_id"].value_counts().head())
    sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns])

Citation

bibtex

@misc{electric_sheep_africa_africa_niger_flood_hazard_data_for_disaster_risk_assessment_selected_co_334c4a27_2018,
  title        = {Flood: Hazard Data for Disaster Risk Assessment (selected countries) | Africa (Niger official open data)},
  author       = {ETH Zürich - Weather and Climate Risks},
  year         = {2018},
  url          = {https://data.humdata.org/dataset/climada-flood-dataset},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-niger-flood-hazard-data-for-disaster-risk-assessment-selected-co-334c4a27}}
}

License

Released under CC BY 4.0.

Original data (c) ETH Zürich - Weather and Climate Risks. When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging.

About Electric Sheep

Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on Hugging Face. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepafrica

Provenance: ingested 2026-08-14 via the Electric Sheep pipeline. Source URL: https://data.humdata.org/dataset/46c703fe-7ba1-484b-a38c-8c53f0cf00c4/resource/5b49391f-4251-4e5b-977a-ee1ea49e2034/download/admin1-timeseries-summaries-flood.csv