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electricsheepasia/asia-owid-use-of-interpolation-and-extrapolation-on-gini-data

Use Of Interpolation And Extrapolation On Gini Data | Asia (Our World in Data) 🌏 630 observations Β· 42 Asia countries Β· 1820–2017 Β· Repackaged by Electric Sheep Asia TL;DR This dataset contains 630 observations of Use Of Interpolation And Extrapolation On Gini Data data across 42 Asia 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… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepasia/asia-owid-use-of-interpolation-and-extrapolation-on-gini-data.

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Dataset Card

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

🌏 630 observations Β· 42 Asia countries Β· 1820–2017 Β· Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)

rows countries years license

TL;DR

This dataset contains 630 observations of Use Of Interpolation And Extrapolation On Gini Data data across 42 Asia countries, spanning 1820–2017.

About the source

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

Geographic coverage

42 Asia countries Β· top rows shown below, sorted by row count:

CountryRowsFirst yearLast year
AFG1518202017
ARM1518202017
AZE1518202017
BGD1518202017
BHR1518202017
CHN1518202017
CYP1518202017
GEO1518202017
IDN1518202017
IND1518202017
IRN1518202017
IRQ1518202017
ISR1518202017
JOR1518202017
JPN1518202017
...27 more countries

Schema

ColumnTypeDescriptionExample
country_namestringβ€”Afghanistan
country_iso3stringβ€”AFG
yearint64β€”1820
GDP per capita (2011 int-$)float64β€”450.096
Baseline Gini seriesfloat64β€”0.5771468
Populationfloat64β€”3288817.0
baseline_GCIP_income_vZ_sourcestringβ€”regional average

Usage

python
from datasets import load_dataset

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

Filter to one country

python
indonesia = df[df["country_iso3"] == "IDN"]

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{asia_owid_use_of_interpolation_and_extrapolation_on_gini_data_2017,
  title        = {Use Of Interpolation And Extrapolation On Gini Data | Asia (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 Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-owid-use-of-interpolation-and-extrapolation-on-gini-data}}
}

License

Released under cc-by-4.0.

Original data Β© Our World in Data. When using this dataset, please cite both the original source above and the Electric Sheep Asia repackaging.

About Electric Sheep

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


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