electricsheepeurope/europe-worldbank-health

Health — Europe (World Bank WDI)

🇪🇺 319,347 observations · 44 Europe countries · 1960–2025 · Repackaged by Electric Sheep Europe

TL;DR

This dataset contains 319,347 observations of Health data across 44 Europe countries, spanning 1960–2025, covering 235 distinct indicators.

About the source

The World Bank's World Development Indicators (WDI) is the world's most-cited reference for global development data. It compiles officially-recognized international statistics from national accounts, government agencies, and partner organizations (IMF, WHO, FAO, UNESCO, ILO, UN agencies). The WDI covers over 1,400 indicators across 21 topics for 217 economies, with annual updates as new data become available.

• Source: World Bank
• Publisher: World Bank Group
• License: cc-by-4.0
• Topic: Health

Methodology

Data is pulled directly from the World Bank's public API (api.worldbank.org/v2) for every indicator tagged to this topic across all countries in the region. Records with no observed value are dropped. Years are normalized to integers; values are coerced to float64.

Geographic coverage

44 Europe countries · top rows shown below, sorted by row count:

Country	Rows	First year	Last year
PRT	7,805	1960	2025
ROU	7,770	1960	2025
IRL	7,761	1960	2025
NLD	7,725	1960	2025
BGR	7,663	1960	2025
MDA	7,663	1960	2025
LTU	7,656	1960	2025
GRC	7,648	1960	2025
LUX	7,624	1960	2025
ALB	7,579	1960	2025
EST	7,554	1960	2025
LVA	7,553	1960	2025
MKD	7,546	1960	2025
GBR	7,546	1960	2025
CHE	7,545	1960	2025
...	29 more countries

Indicators (sample)

• SG.VAW.1549.ZS — Proportion of women subjected to physical and/or sexual violence in the last 12 months (% of ever-partnered women ages 15-49)
• SG.VAW.ARGU.ZS — Women who believe a husband is justified in beating his wife when she argues with him (%)
• SG.VAW.BURN.ZS — Women who believe a husband is justified in beating his wife when she burns the food (%)
• SG.VAW.GOES.ZS — Women who believe a husband is justified in beating his wife when she goes out without telling him (%)
• SG.VAW.NEGL.ZS — Women who believe a husband is justified in beating his wife when she neglects the children (%)
• SG.VAW.REAS.ZS — Women who believe a husband is justified in beating his wife (any of five reasons) (%)
• SG.VAW.REFU.ZS — Women who believe a husband is justified in beating his wife when she refuses sex with him (%)
• SH.ALC.PCAP.FE.LI — Total alcohol consumption per capita, female (liters of pure alcohol, projected estimates, female 15+ years of age)
• SH.ALC.PCAP.LI — Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)
• SH.ALC.PCAP.MA.LI — Total alcohol consumption per capita, male (liters of pure alcohol, projected estimates, male 15+ years of age)
• ... and 225 more indicators

Schema

Column	Type	Description	Example
indicator_id	string	World Bank WDI indicator code (e.g. SP.POP.TOTL)	SG.VAW.GOES.ZS
indicator_name	string	Human-readable indicator name	Women who believe a husband is justif…
country_iso2	string	ISO 3166-1 alpha-2 country code	AL
country_iso3	string	ISO 3166-1 alpha-3 country code (preferred join key)	ALB
country_name	string	English country name as published by the World Bank	Albania
year	int64	Year of observation (CE)	2018
value	float64	Observed indicator value	3.7
unit	string	Unit of measurement (often empty)	``
obs_status	string	World Bank observation-status flag (e.g. preliminary, estimated)	``

Data quality & caveats

• Records with null observations are filtered out at ingest.
• Indicator coverage varies by country and year; not every (indicator × country × year) combination is present.
• Some countries appear under multiple aggregate groupings in WB; this dataset is filtered to individual sovereign states only.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepeurope/europe-worldbank-health")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

germany = df[df["country_iso3"] == "DEU"]

Time-series for a single indicator

sample = (df[df["indicator_id"] == "SG.VAW.GOES.ZS"]
          .sort_values("year"))
sample.plot(x="year", y="value", title="SG.VAW.GOES.ZS")

Pivot to country × year matrix

matrix = (df[df["indicator_id"] == "SG.VAW.GOES.ZS"]
          .pivot_table(index="year", columns="country_iso3", values="value"))
print(matrix.tail())

Citation

@misc{europe_worldbank_health_2025,
  title        = {Health — Europe (World Bank WDI)},
  author       = {World Bank Group},
  year         = {2025},
  url          = {https://databank.worldbank.org/source/world-development-indicators},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Europe},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-worldbank-health}}
}

License

Released under cc-by-4.0.

Original data © World Bank Group. When using this dataset, please cite both the original source above and the Electric Sheep Europe repackaging.

About Electric Sheep

Electric Sheep Europe is part of the Electric Sheep mission: a unified, ML-ready data layer for Europe 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/electricsheepeurope

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Provenance: ingested 2026-05-24 via the Electric Sheep pipeline. Source URL: https://databank.worldbank.org/source/world-development-indicators