Polars LazyFrame 完全ガイド 2026:遅延評価とストリーミングで大規模データを高速処理
Polars LazyFrameの遅延評価とnew-streamingエンジンで、100GB級のParquetをチャンク処理する方法を解説。pandasからの移行手順、explain()の読み方、本番チューニング、よくある落とし穴まで実戦コード付きで網羅。
Priya is a senior data engineer with 11 years building analytics platforms, most recently at Stripe where she led the migration of the merchant analytics pipeline from pandas to polars (cut p95 batch latency from 42 minutes to under 6). Before Stripe she spent four years at Mode Analytics writing the query engine that powered customer dashboards, and two years at Etsy on the seller-insights team. She writes mainly about polars internals, lazy evaluation patterns, and the practical edges of moving production pandas code to polars without breaking analyst muscle memory. Her side project is a 12k-row benchmark suite comparing pandas 2.x, polars, and DuckDB across realistic e-commerce joins. Priya lives in Oakland, mentors through Women in Data, and is slowly learning to play go.
Polars LazyFrameの遅延評価とnew-streamingエンジンで、100GB級のParquetをチャンク処理する方法を解説。pandasからの移行手順、explain()の読み方、本番チューニング、よくある落とし穴まで実戦コード付きで網羅。