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electricsheepeurope/europe-owid-use-of-interpolation-and-extrapolation-on-gini-data

Use Of Interpolation And Extrapolation On Gini Data | Europe (Our World in Data) 🇪🇺 555 observations · 37 Europe countries · 1820–2017 · Repackaged by Electric Sheep Europe TL;DR This dataset contains 555 observations of Use Of Interpolation And Extrapolation On Gini Data data across 37 Europe countries, spanning 1820–2017. About the source Source: Our World in Data Publisher: Our World in Data License: cc-by-4.0 Topic: Use Of Interpolation… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepeurope/europe-owid-use-of-interpolation-and-extrapolation-on-gini-data.

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Dataset Card

Use Of Interpolation And Extrapolation On Gini Data | Europe (Our World in Data)

🇪🇺 555 observations · 37 Europe countries · 1820–2017 · Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)

TL;DR

This dataset contains 555 observations of Use Of Interpolation And Extrapolation On Gini Data data across 37 Europe countries, spanning 1820–2017.

About the source

—Source: Our World in Data
—Publisher: Our World in Data
—License: cc-by-4.0
—Topic: Use Of Interpolation And Extrapolation On Gini Data

Geographic coverage

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

Country	Rows	First year	Last year
`ALB`	15	1820	2017
`AUT`	15	1820	2017
`BEL`	15	1820	2017
`BGR`	15	1820	2017
`BIH`	15	1820	2017
`BLR`	15	1820	2017
`CHE`	15	1820	2017
`DEU`	15	1820	2017
`DNK`	15	1820	2017
`ESP`	15	1820	2017
`EST`	15	1820	2017
`FIN`	15	1820	2017
`FRA`	15	1820	2017
`GBR`	15	1820	2017
`GRC`	15	1820	2017
...	22 more countries

Schema

Column	Type	Description	Example
`country_name`	`string`	—	`Albania`
`country_iso3`	`string`	—	`ALB`
`year`	`int64`	—	`1820`
`GDP per capita (2011 int-$)`	`float64`	—	`484.19757`
`Baseline Gini series`	`float64`	—	`0.46546036`
`Population`	`float64`	—	`437026.0`
`baseline_GCIP_income_vZ_source`	`string`	—	`regional average`

Usage

python

from datasets import load_dataset

ds = load_dataset("electricsheepeurope/europe-owid-use-of-interpolation-and-extrapolation-on-gini-data")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

python

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

Time-series for a single indicator

python

sample = df.sort_values("year")
sample.plot(x="year", y="GDP per capita (2011 int-$)")

Citation

bibtex

@misc{europe_owid_use_of_interpolation_and_extrapolation_on_gini_data_2017,
  title        = {Use Of Interpolation And Extrapolation On Gini Data | Europe (Our World in Data)},
  author       = {Our World in Data},
  year         = {2017},
  url          = {https://ourworldindata.org/grapher/use-of-interpolation-and-extrapolation-on-gini-data},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Europe},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-owid-use-of-interpolation-and-extrapolation-on-gini-data}}
}

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 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

Provenance: ingested 2026-06-12 via the Electric Sheep pipeline. Source URL: https://ourworldindata.org/grapher/use-of-interpolation-and-extrapolation-on-gini-data