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country_name
stringclasses
44 values
country_iso3
stringclasses
44 values
year
int64
1.98k
2.02k
Obesity among adults, BMI >= 30 kg/m2 (age-standardized estimate) (%) - Sex: both sexes - Age group: 18+ years of age
float64
0.28
43.9
Algeria
DZA
1,980
7.566391
Algeria
DZA
1,981
7.763365
Algeria
DZA
1,982
7.967383
Algeria
DZA
1,983
8.177412
Algeria
DZA
1,984
8.392967
Algeria
DZA
1,985
8.61531
Algeria
DZA
1,986
8.845652
Algeria
DZA
1,987
9.087521
Algeria
DZA
1,988
9.340279
Algeria
DZA
1,989
9.603335
Algeria
DZA
1,990
9.876724
Algeria
DZA
1,991
10.159869
Algeria
DZA
1,992
10.458332
Algeria
DZA
1,993
10.771078
Algeria
DZA
1,994
11.097379
Algeria
DZA
1,995
11.442178
Algeria
DZA
1,996
11.804875
Algeria
DZA
1,997
12.18662
Algeria
DZA
1,998
12.581797
Algeria
DZA
1,999
12.986715
Algeria
DZA
2,000
13.403484
Algeria
DZA
2,001
13.831623
Algeria
DZA
2,002
14.267319
Algeria
DZA
2,003
14.705078
Algeria
DZA
2,004
15.143351
Algeria
DZA
2,005
15.579513
Algeria
DZA
2,006
16.016317
Algeria
DZA
2,007
16.458
Algeria
DZA
2,008
16.908522
Algeria
DZA
2,009
17.366306
Algeria
DZA
2,010
17.829445
Algeria
DZA
2,011
18.294413
Algeria
DZA
2,012
18.7619
Algeria
DZA
2,013
19.230501
Algeria
DZA
2,014
19.69693
Algeria
DZA
2,015
20.160908
Algeria
DZA
2,016
20.623337
Algeria
DZA
2,017
21.052338
Algeria
DZA
2,018
21.441952
Algeria
DZA
2,019
21.805805
Algeria
DZA
2,020
22.159735
Algeria
DZA
2,021
22.507175
Algeria
DZA
2,022
22.852419
Algeria
DZA
2,023
23.205303
Algeria
DZA
2,024
23.56664
Angola
AGO
1,980
1.726724
Angola
AGO
1,981
1.814182
Angola
AGO
1,982
1.90852
Angola
AGO
1,983
2.010154
Angola
AGO
1,984
2.119026
Angola
AGO
1,985
2.235081
Angola
AGO
1,986
2.359098
Angola
AGO
1,987
2.492131
Angola
AGO
1,988
2.634193
Angola
AGO
1,989
2.785198
Angola
AGO
1,990
2.945407
Angola
AGO
1,991
3.11454
Angola
AGO
1,992
3.292839
Angola
AGO
1,993
3.482129
Angola
AGO
1,994
3.68286
Angola
AGO
1,995
3.892634
Angola
AGO
1,996
4.113257
Angola
AGO
1,997
4.34787
Angola
AGO
1,998
4.59716
Angola
AGO
1,999
4.860971
Angola
AGO
2,000
5.134833
Angola
AGO
2,001
5.417082
Angola
AGO
2,002
5.705215
Angola
AGO
2,003
5.995956
Angola
AGO
2,004
6.287131
Angola
AGO
2,005
6.576645
Angola
AGO
2,006
6.862202
Angola
AGO
2,007
7.142585
Angola
AGO
2,008
7.416741
Angola
AGO
2,009
7.683521
Angola
AGO
2,010
7.945053
Angola
AGO
2,011
8.205835
Angola
AGO
2,012
8.47196
Angola
AGO
2,013
8.748066
Angola
AGO
2,014
9.038564
Angola
AGO
2,015
9.345714
Angola
AGO
2,016
9.668779
Angola
AGO
2,017
10.003951
Angola
AGO
2,018
10.349412
Angola
AGO
2,019
10.706406
Angola
AGO
2,020
11.076964
Angola
AGO
2,021
11.459894
Angola
AGO
2,022
11.855334
Angola
AGO
2,023
12.264708
Angola
AGO
2,024
12.688406
Benin
BEN
1,980
1.103784
Benin
BEN
1,981
1.176242
Benin
BEN
1,982
1.254922
Benin
BEN
1,983
1.340381
Benin
BEN
1,984
1.432738
Benin
BEN
1,985
1.532687
Benin
BEN
1,986
1.64048
Benin
BEN
1,987
1.756629
Benin
BEN
1,988
1.88195
Benin
BEN
1,989
2.017315
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Obesity Prevalence Adults Who Gho | Africa (Our World in Data) | Africa (World Health Organization)

Size category: 1K<n<10K - Formats: parquet - Sector: other_unclassified - 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: Obesity Prevalence Adults Who Gho | Africa (Our World in Data) 🌍 2,430 observations · 54 Africa countries · 1980–2024 · Repackaged by Electric Sheep Africa TL;DR This dataset contains 2,430 observations of Obesity Prevalence Adults Who Gho data across 54 Africa countries, spanning 1980–2024. About the source Source: Our World in Data Publisher: Our World in Data License: cc-by-4.0 Topic: Obesity Prevalence Adults Who Gho… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-owid-obesity-prevalence-adults-who-gho.

Dataset Profile

Field Value
Hugging Face repo electricsheepafrica/africa-owid-obesity-prevalence-adults-who-gho
Sector other_unclassified
Topic tags tabular, our-world-in-data, obesity-prevalence-adults-who-gho, owid, long-run-series, time-series
Modalities tabular, text
Formats parquet
Size category 1K<n<10K
Countries Africa-wide or source-defined African coverage
ISO3 coverage not declared
Last modified on HF 2026-06-06 01:51:07+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-obesity-prevalence-adults-who-gho")
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, sector, 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_obesity_prevalence_adults_who_gho_2026,
  title        = {Obesity Prevalence Adults Who Gho | Africa (Our World in Data) | Africa (World Health Organization)},
  author       = {WHO public data},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-owid-obesity-prevalence-adults-who-gho},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-owid-obesity-prevalence-adults-who-gho}}
}

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.

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