"""Minimal, runnable example: load and analyse the dataset straight from HF.

Requires: pandas, pyarrow
    pip install pandas pyarrow

Run:
    python examples/quickstart.py
"""
import pandas as pd

BASE = "hf://datasets/skyleee/diversa-biomedical-v1"


def main():
    works = pd.read_parquet(
        f"{BASE}/data/works.parquet",
        columns=["work_id", "publication_year", "field_name", "cited_by_count",
                 "is_oa", "international_collaboration"],
    )
    print(f"works: {len(works):,}")
    print(f"years: {works.publication_year.min()}-{works.publication_year.max()}")
    print(f"open access share: {works.is_oa.mean():.1%}")
    print(f"international collaboration rate: {works.international_collaboration.mean():.1%}")

    print("\nworks per field:")
    print(works.field_name.value_counts().head(10).to_string())

    print("\nmost-cited topics (dimension table):")
    topics = pd.read_parquet(f"{BASE}/data/topics.parquet",
                             columns=["topic_name", "works_count", "total_citations"])
    print(topics.sort_values("total_citations", ascending=False).head(10).to_string(index=False))


if __name__ == "__main__":
    main()