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README.md
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dtype: float64
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- name: Millet
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dtype: float64
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- name: Sorghum
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dtype: float64
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- name: Potatoes
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dtype: float64
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splits:
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- name: train
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num_bytes: 158970
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num_examples: 1596
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- name: test
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num_bytes: 40166
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num_examples: 399
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download_size: 129784
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dataset_size: 199136
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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- split: test
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path: data/test-*
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---
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---
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license: cc-by-4.0
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language:
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- en
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task_categories:
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- tabular-classification
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- tabular-regression
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- time-series-forecasting
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multilinguality: monolingual
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size_categories:
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- 1K<n<10K
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tags:
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- tabular
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- europe
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- our-world-in-data
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- yields-of-important-staple-crops
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- owid
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- long-run-series
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- time-series
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pretty_name: "Yields Of Important Staple Crops | Europe (Our World in Data)"
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---
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# Yields Of Important Staple Crops | Europe (Our World in Data)
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🇪🇺 **1,995 observations** · **39 Europe countries** · **1275–2024** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*
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## TL;DR
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This dataset contains **1,995 observations** of `Yields Of Important Staple Crops` data across **39 Europe countries**, spanning **1275–2024**.
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## About the source
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- **Source:** [Our World in Data](https://ourworldindata.org/grapher/yields-of-important-staple-crops)
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- **Publisher:** Our World in Data
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- **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/)
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- **Topic:** Yields Of Important Staple Crops
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## Geographic coverage
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39 Europe countries · top rows shown below, sorted by row count:
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| Country | Rows | First year | Last year |
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|---------|-----:|-----------:|----------:|
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| `GBR` | 81 | 1275 | 2024 |
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| `GRC` | 68 | 1850 | 2024 |
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| `AUT` | 68 | 1850 | 2024 |
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| `DNK` | 68 | 1850 | 2024 |
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| `ESP` | 68 | 1850 | 2024 |
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| `DEU` | 68 | 1850 | 2024 |
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| `FRA` | 68 | 1850 | 2024 |
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| `NLD` | 68 | 1850 | 2024 |
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| `NOR` | 68 | 1850 | 2024 |
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| `ITA` | 68 | 1850 | 2024 |
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| `ROU` | 67 | 1911 | 2024 |
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| `HUN` | 67 | 1911 | 2024 |
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| `BGR` | 67 | 1911 | 2024 |
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| `ALB` | 64 | 1961 | 2024 |
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| `CHE` | 64 | 1961 | 2024 |
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| ... | _24 more countries_ | | |
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## Schema
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| Column | Type | Description | Example |
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|--------|------|-------------|---------|
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| `country_name` | `string` | — | `Albania` |
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| `country_iso3` | `string` | — | `ALB` |
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| `year` | `int64` | — | `1961` |
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| `Wheat` | `float64` | — | `0.7731` |
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| `Rice` | `float64` | — | `1.5514001` |
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| `Barley` | `float64` | — | `1.0224` |
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| `Maize` | `float64` | — | `0.9541` |
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| `Rye` | `float64` | — | `0.6626` |
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| `Oats` | `float64` | — | `0.60450006` |
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| `Millet` | `float64` | — | `1.5315001` |
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| `Sorghum` | `float64` | — | `0.53790003` |
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| `Potatoes` | `float64` | — | `7.2134004` |
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## Data quality & caveats
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- `Wheat` column has 3.6% null values (filtered to non-null in this dataset).
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## Usage
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```python
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from datasets import load_dataset
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ds = load_dataset("electricsheepeurope/europe-owid-yields-of-important-staple-crops")
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df = ds["train"].to_pandas()
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print(df.head())
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```
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### Filter to one country
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```python
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germany = df[df["country_iso3"] == "DEU"]
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```
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### Time-series for a single indicator
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```python
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sample = df.sort_values("year")
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sample.plot(x="year", y="Wheat")
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```
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## Citation
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```bibtex
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@misc{europe_owid_yields_of_important_staple_crops_2024,
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title = {Yields Of Important Staple Crops | Europe (Our World in Data)},
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author = {Our World in Data},
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year = {2024},
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url = {https://ourworldindata.org/grapher/yields-of-important-staple-crops},
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publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe},
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howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-owid-yields-of-important-staple-crops}}
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}
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```
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## License
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Released under [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/).
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Original data © Our World in Data. When using this dataset, please cite both the original source above and the Electric Sheep Europe repackaging.
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## About Electric Sheep
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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.
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Browse the full collection: [huggingface.co/electricsheepeurope](https://huggingface.co/electricsheepeurope)
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---
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_Provenance: ingested 2026-06-13 via the Electric Sheep pipeline. Source URL: https://ourworldindata.org/grapher/yields-of-important-staple-crops_
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