{"slug":"jaechang-hits-polars-dataframes","source_name":"jaechang-hits/polars-dataframes","name":"Jaechang Hits/Polars Dataframes","description":"Fast in-memory DataFrame with lazy evaluation, parallel execution, Arrow backend. Use for tabular data in RAM (1–100 GB) when pandas is too slow. Expression API: select, filter, group_by, joins, pivots, window. Lazy mode enables predicate/projection pushdown. Reads CSV, Parquet, JSON, Excel, DBs, cloud. Larger-than-RAM: Dask; GPU: cuDF.","version":1,"lift":{"pass_rate_delta_pts":4.55,"pass_rate_pct":95.5,"total_cases":22,"passed_cases":21,"tokens_delta_pct":659.5,"turns_delta_pct":0,"verdict":"mixed","benchmark_model":"gemini-3.6-flash","grading_method":"judged","completed_at":"2026-08-23T23:18:21.182684+00:00"},"skill_score":0.9545,"benchmark_models":[{"model":"gemini-3.6-flash","headline":true,"delta_pts":4.55,"with_pass_pct":95.5,"without_pass_pct":90.9,"tokens_delta_pct":659.5,"turns_delta_pct":0,"total_cases":22,"cases_aggregated":22,"verdict":"mixed","never_hurt":true,"completed_at":"2026-08-23T23:18:21.182684+00:00","run_id":"e814cff3-365b-42c2-8f8a-7d860aca8602","version_number":1,"is_latest_version":true,"gate":null}],"trust":{"skill_safety":"passed","safety_status":"clean","intent_verdict":"safe","content_status":"clean","indexable":true},"license":"MIT","install_count":0,"manifest_hash":"28f726cd0580af49fa9119ba6867b76e9d18c178a6cdbde78d5f5c2e4f5862fd","raw_url":"https://app.decimal.ai/s/jaechang-hits-polars-dataframes/SKILL.md","scorecard_url":"https://app.decimal.ai/skills/jaechang-hits-polars-dataframes"}