Dataset Viewer
Auto-converted to Parquet Duplicate
country_name
stringclasses
36 values
country_iso3
stringclasses
36 values
year
int64
2.01k
2.02k
Direct economic loss attributed to disasters relative to GDP (%)
float64
0
2.8
Algeria
DZA
2,005
0
Algeria
DZA
2,006
0
Algeria
DZA
2,007
0
Algeria
DZA
2,008
0
Algeria
DZA
2,009
0
Algeria
DZA
2,010
0
Algeria
DZA
2,011
0
Algeria
DZA
2,012
0
Algeria
DZA
2,013
0
Algeria
DZA
2,014
0
Algeria
DZA
2,015
0
Algeria
DZA
2,016
0
Algeria
DZA
2,017
0
Algeria
DZA
2,018
0
Algeria
DZA
2,019
0
Algeria
DZA
2,020
0
Algeria
DZA
2,021
0
Algeria
DZA
2,022
0
Algeria
DZA
2,023
0
Angola
AGO
2,005
0
Angola
AGO
2,006
0
Angola
AGO
2,007
0
Angola
AGO
2,008
0
Angola
AGO
2,009
0
Angola
AGO
2,010
0
Angola
AGO
2,011
0
Angola
AGO
2,012
0
Angola
AGO
2,013
0
Angola
AGO
2,014
0.02
Angola
AGO
2,015
0
Angola
AGO
2,016
0.01
Angola
AGO
2,017
0
Angola
AGO
2,018
0.02
Angola
AGO
2,019
0.04
Angola
AGO
2,020
0
Benin
BEN
2,010
0.24
Benin
BEN
2,011
0.02
Benin
BEN
2,012
0.01
Benin
BEN
2,013
0
Benin
BEN
2,014
0
Benin
BEN
2,015
0
Benin
BEN
2,016
0
Benin
BEN
2,017
0.06
Benin
BEN
2,018
0.01
Benin
BEN
2,019
0.01
Benin
BEN
2,020
0
Botswana
BWA
2,013
0
Botswana
BWA
2,015
0
Botswana
BWA
2,016
0
Botswana
BWA
2,017
0
Botswana
BWA
2,018
0
Burkina Faso
BFA
2,005
0
Burkina Faso
BFA
2,007
0.06
Burkina Faso
BFA
2,009
0.08
Burkina Faso
BFA
2,010
0.01
Burkina Faso
BFA
2,012
0
Burkina Faso
BFA
2,013
0
Burkina Faso
BFA
2,014
0
Burkina Faso
BFA
2,015
0.01
Burkina Faso
BFA
2,016
0.01
Burkina Faso
BFA
2,017
0.01
Burkina Faso
BFA
2,018
0
Burundi
BDI
2,015
0.01
Burundi
BDI
2,016
0.01
Burundi
BDI
2,017
0.01
Burundi
BDI
2,018
0.05
Burundi
BDI
2,019
0.58
Burundi
BDI
2,020
0.19
Burundi
BDI
2,021
0.11
Burundi
BDI
2,022
0.07
Burundi
BDI
2,023
0.04
Cameroon
CMR
2,006
0
Cameroon
CMR
2,007
0
Cameroon
CMR
2,008
0
Cameroon
CMR
2,009
0
Cameroon
CMR
2,014
0
Cameroon
CMR
2,016
0
Cameroon
CMR
2,017
0
Cameroon
CMR
2,018
0
Cameroon
CMR
2,019
0.02
Cape Verde
CPV
2,006
0
Cape Verde
CPV
2,008
0
Cape Verde
CPV
2,009
0
Cape Verde
CPV
2,014
0
Cape Verde
CPV
2,015
0
Cape Verde
CPV
2,017
0
Comoros
COM
2,006
0
Comoros
COM
2,008
0
Comoros
COM
2,012
0.01
Comoros
COM
2,013
0
Comoros
COM
2,014
0
Comoros
COM
2,015
0
Comoros
COM
2,017
0
Comoros
COM
2,018
0
Comoros
COM
2,019
0.1
Comoros
COM
2,022
0
Comoros
COM
2,024
0
Cote d'Ivoire
CIV
2,005
0
Cote d'Ivoire
CIV
2,006
0
Cote d'Ivoire
CIV
2,007
0
End of preview. Expand in Data Studio

Direct Disaster Loss As A Share Of Gdp | Africa (Our World in Data) | Africa (Electric Sheep Africa metadata inventory)

Size category: n<1K - Formats: parquet - Sector: economics_finance - Engineered by Electric Sheep Africa

size sector downloads license

TL;DR

This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.

What This Dataset Covers

Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.

Dataset context from the existing Hugging Face card: Direct Disaster Loss As A Share Of Gdp | Africa (Our World in Data) 🌍 490 observations · 45 Africa countries · 2005–2024 · Repackaged by Electric Sheep Africa TL;DR This dataset contains 490 observations of Direct Disaster Loss As A Share Of Gdp data across 45 Africa countries, spanning 2005–2024. About the source Source: Our World in Data Publisher: Our World in Data License: cc-by-4.0 Topic: Direct Disaster Loss As A Share Of Gdp… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-owid-direct-disaster-loss-as-a-share-of-gdp.

Dataset Profile

Field Value
Hugging Face repo electricsheepafrica/africa-owid-direct-disaster-loss-as-a-share-of-gdp
Sector economics_finance
Topic tags tabular, our-world-in-data, direct-disaster-loss-as-a-share-of-gdp, owid, long-run-series, time-series
Modalities tabular, text
Formats parquet
Size category n<1K
Countries Africa-wide or source-defined African coverage
ISO3 coverage not declared
Last modified on HF 2026-06-03 20:35:10+00:00
Inventory snapshot 2026-07-16T16:00:34Z

How To Read This Dataset

  • Start from the repository files and the dataset viewer when available.
  • Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
  • Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
  • Preserve missing values until you have a defensible imputation rule.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-owid-direct-disaster-loss-as-a-share-of-gdp")
print(ds)

split_name = next(iter(ds))
table = ds[split_name]
print(table.features)
print(table[:3])

Convert To Pandas When Tabular

from datasets import Dataset

first_split = ds[next(iter(ds))]
if isinstance(first_split, Dataset):
    df = first_split.to_pandas()
    print(df.head())

Data Quality Notes

  • This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
  • Exact schema, row counts, and source files should be inspected in the repository data files.
  • Metadata gaps from the inventory: country, upstream_publisher.
  • Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.

Source And Provenance

Suggested Analyses

  • Inspect schema and missingness before modeling.
  • Profile variables by geography, time, and subgroup columns where present.
  • Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
  • Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.

Citation

@misc{electric_sheep_africa_africa_owid_direct_disaster_loss_as_a_share_of_gdp_2026,
  title        = {Direct Disaster Loss As A Share Of Gdp | Africa (Our World in Data) | Africa (Electric Sheep Africa metadata inventory)},
  author       = {Public dataset metadata},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-owid-direct-disaster-loss-as-a-share-of-gdp},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-owid-direct-disaster-loss-as-a-share-of-gdp}}
}

License

Released under CC BY 4.0.

Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.

About Electric Sheep Africa

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


Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: catalog/esa_metadata_inventory/master_metadata.jsonl.

Downloads last month
21