The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
Error code: JobManagerCrashedError
Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
country_name string | country_iso3 string | year int64 | Adult obesity float64 | Daily protein supply float64 | World region according to OWID string |
|---|---|---|---|---|---|
Algeria | DZA | 1,961 | null | 44.747276 | Africa |
Algeria | DZA | 1,962 | null | 43.703197 | Africa |
Algeria | DZA | 1,963 | null | 40.77703 | Africa |
Algeria | DZA | 1,964 | null | 40.976738 | Africa |
Algeria | DZA | 1,965 | null | 43.478893 | Africa |
Algeria | DZA | 1,966 | null | 43.371334 | Africa |
Algeria | DZA | 1,967 | null | 45.209724 | Africa |
Algeria | DZA | 1,968 | null | 47.482635 | Africa |
Algeria | DZA | 1,969 | null | 47.57047 | Africa |
Algeria | DZA | 1,970 | null | 46.92816 | Africa |
Algeria | DZA | 1,971 | null | 47.835354 | Africa |
Algeria | DZA | 1,972 | null | 52.37302 | Africa |
Algeria | DZA | 1,973 | null | 53.500656 | Africa |
Algeria | DZA | 1,974 | null | 56.493095 | Africa |
Algeria | DZA | 1,975 | null | 58.92425 | Africa |
Algeria | DZA | 1,976 | null | 58.9511 | Africa |
Algeria | DZA | 1,977 | null | 59.991337 | Africa |
Algeria | DZA | 1,978 | null | 64.01775 | Africa |
Algeria | DZA | 1,979 | null | 65.083664 | Africa |
Algeria | DZA | 1,980 | null | 68.74327 | Africa |
Algeria | DZA | 1,981 | null | 67.94525 | Africa |
Algeria | DZA | 1,982 | null | 68.574196 | Africa |
Algeria | DZA | 1,983 | null | 70.20865 | Africa |
Algeria | DZA | 1,984 | null | 68.859604 | Africa |
Algeria | DZA | 1,985 | null | 72.25155 | Africa |
Algeria | DZA | 1,986 | null | 73.75632 | Africa |
Algeria | DZA | 1,987 | null | 74.185814 | Africa |
Algeria | DZA | 1,988 | null | 74.974686 | Africa |
Algeria | DZA | 1,989 | null | 76.86174 | Africa |
Algeria | DZA | 1,990 | 8.58711 | 76.17886 | Africa |
Algeria | DZA | 1,991 | 8.8655 | 76.414406 | Africa |
Algeria | DZA | 1,992 | 9.15763 | 79.08337 | Africa |
Algeria | DZA | 1,993 | 9.46155 | 80.56043 | Africa |
Algeria | DZA | 1,994 | 9.77856 | 78.606766 | Africa |
Algeria | DZA | 1,995 | 10.11147 | 77.946266 | Africa |
Algeria | DZA | 1,996 | 10.44221 | 77.36477 | Africa |
Algeria | DZA | 1,997 | 10.76643 | 74.869194 | Africa |
Algeria | DZA | 1,998 | 11.12044 | 80.69525 | Africa |
Algeria | DZA | 1,999 | 11.50779 | 81.14996 | Africa |
Algeria | DZA | 2,000 | 11.90928 | 77.87112 | Africa |
Algeria | DZA | 2,001 | 12.32476 | 79.371376 | Africa |
Algeria | DZA | 2,002 | 12.75255 | 81.979225 | Africa |
Algeria | DZA | 2,003 | 13.18989 | 84.2722 | Africa |
Algeria | DZA | 2,004 | 13.63663 | 85.569496 | Africa |
Algeria | DZA | 2,005 | 14.09408 | 86.27374 | Africa |
Algeria | DZA | 2,006 | 14.55953 | 85.9301 | Africa |
Algeria | DZA | 2,007 | 15.03776 | 85.41886 | Africa |
Algeria | DZA | 2,008 | 15.53641 | 85.626396 | Africa |
Algeria | DZA | 2,009 | 16.05635 | 88.70616 | Africa |
Algeria | DZA | 2,010 | 16.59766 | 90.11 | Africa |
Algeria | DZA | 2,011 | 17.15904 | 91.290016 | Africa |
Algeria | DZA | 2,012 | 17.74237 | 93.98002 | Africa |
Algeria | DZA | 2,013 | 18.35061 | 93.81999 | Africa |
Algeria | DZA | 2,014 | 18.98311 | 93.869995 | Africa |
Algeria | DZA | 2,015 | 19.63806 | 93.810005 | Africa |
Algeria | DZA | 2,016 | 20.3156 | 92.76 | Africa |
Algeria | DZA | 2,017 | 20.98617 | 91.18 | Africa |
Algeria | DZA | 2,018 | 21.64547 | 93.250015 | Africa |
Algeria | DZA | 2,019 | 22.30026 | 94.65 | Africa |
Algeria | DZA | 2,020 | 22.95371 | 92.81999 | Africa |
Algeria | DZA | 2,021 | 23.60328 | 94.15001 | Africa |
Algeria | DZA | 2,022 | 24.24859 | 95.37 | Africa |
Algeria | DZA | 2,023 | null | 93.58999 | Africa |
Angola | AGO | 1,961 | null | 34.92103 | Africa |
Angola | AGO | 1,962 | null | 36.040184 | Africa |
Angola | AGO | 1,963 | null | 36.512955 | Africa |
Angola | AGO | 1,964 | null | 39.247272 | Africa |
Angola | AGO | 1,965 | null | 38.309727 | Africa |
Angola | AGO | 1,966 | null | 38.600136 | Africa |
Angola | AGO | 1,967 | null | 40.971 | Africa |
Angola | AGO | 1,968 | null | 39.41479 | Africa |
Angola | AGO | 1,969 | null | 40.864483 | Africa |
Angola | AGO | 1,970 | null | 41.901615 | Africa |
Angola | AGO | 1,971 | null | 41.769714 | Africa |
Angola | AGO | 1,972 | null | 39.232723 | Africa |
Angola | AGO | 1,973 | null | 37.798244 | Africa |
Angola | AGO | 1,974 | null | 38.605026 | Africa |
Angola | AGO | 1,975 | null | 39.801117 | Africa |
Angola | AGO | 1,976 | null | 40.134083 | Africa |
Angola | AGO | 1,977 | null | 42.993565 | Africa |
Angola | AGO | 1,978 | null | 43.406982 | Africa |
Angola | AGO | 1,979 | null | 42.722042 | Africa |
Angola | AGO | 1,980 | null | 43.293316 | Africa |
Angola | AGO | 1,981 | null | 42.84666 | Africa |
Angola | AGO | 1,982 | null | 41.61606 | Africa |
Angola | AGO | 1,983 | null | 40.837685 | Africa |
Angola | AGO | 1,984 | null | 40.113472 | Africa |
Angola | AGO | 1,985 | null | 42.08927 | Africa |
Angola | AGO | 1,986 | null | 37.672085 | Africa |
Angola | AGO | 1,987 | null | 37.104233 | Africa |
Angola | AGO | 1,988 | null | 35.90366 | Africa |
Angola | AGO | 1,989 | null | 34.20818 | Africa |
Angola | AGO | 1,990 | 2.6377 | 34.184868 | Africa |
Angola | AGO | 1,991 | 2.80296 | 29.991522 | Africa |
Angola | AGO | 1,992 | 2.97692 | 30.058783 | Africa |
Angola | AGO | 1,993 | 3.16063 | 29.593307 | Africa |
Angola | AGO | 1,994 | 3.35392 | 31.587019 | Africa |
Angola | AGO | 1,995 | 3.55431 | 31.040546 | Africa |
Angola | AGO | 1,996 | 3.76282 | 32.529385 | Africa |
Angola | AGO | 1,997 | 3.98193 | 33.660484 | Africa |
Adult Obesity Vs Protein | Africa (Our World in Data)
🌍 3,072 observations · 54 Africa countries · 1961–2023 · Repackaged by Electric Sheep Africa
TL;DR
This dataset contains 3,072 observations of Adult Obesity Vs Protein data across 54 Africa countries, spanning 1961–2023.
About the source
- Source: Our World in Data
- Publisher: Our World in Data
- License: cc-by-4.0
- Topic: Adult Obesity Vs Protein
Geographic coverage
54 Africa countries · top rows shown below, sorted by row count:
| Country | Rows | First year | Last year |
|---|---|---|---|
AGO |
63 | 1961 | 2023 |
BFA |
63 | 1961 | 2023 |
BWA |
63 | 1961 | 2023 |
COG |
63 | 1961 | 2023 |
CMR |
63 | 1961 | 2023 |
CIV |
63 | 1961 | 2023 |
GMB |
63 | 1961 | 2023 |
GNB |
63 | 1961 | 2023 |
GIN |
63 | 1961 | 2023 |
CPV |
63 | 1961 | 2023 |
DZA |
63 | 1961 | 2023 |
DJI |
63 | 1961 | 2023 |
GAB |
63 | 1961 | 2023 |
EGY |
63 | 1961 | 2023 |
GHA |
63 | 1961 | 2023 |
| ... | 39 more countries |
Schema
| Column | Type | Description | Example |
|---|---|---|---|
country_name |
string |
— | Algeria |
country_iso3 |
string |
— | DZA |
year |
int64 |
— | 1961 |
Adult obesity |
float64 |
— | 8.58711 |
Daily protein supply |
float64 |
— | 44.747276 |
World region according to OWID |
string |
— | Africa |
Data quality & caveats
Adult obesitycolumn has 42.0% null values (filtered to non-null in this dataset).
Usage
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-owid-adult-obesity-vs-protein")
df = ds["train"].to_pandas()
print(df.head())
Filter to one country
kenya = df[df["country_iso3"] == "KEN"]
Time-series for a single indicator
sample = df.sort_values("year")
sample.plot(x="year", y="Adult obesity")
Citation
@misc{africa_owid_adult_obesity_vs_protein_2023,
title = {Adult Obesity Vs Protein | Africa (Our World in Data)},
author = {Our World in Data},
year = {2023},
url = {https://ourworldindata.org/grapher/adult-obesity-vs-protein},
publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-owid-adult-obesity-vs-protein}}
}
License
Released under cc-by-4.0.
Original data © Our World in Data. 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 HuggingFace. 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 2026-06-01 via the Electric Sheep pipeline. Source URL: https://ourworldindata.org/grapher/adult-obesity-vs-protein
- Downloads last month
- 36