Polars is a blazing fast DataFrame library that outperforms Pandas 5-20x for large datasets. Key advantages: Rust-based (memory efficient, no GIL), lazy evaluation (query optimization), native parallel processing, Arrow memory format (zero-copy with NumPy/PyTorch). Polars syntax: import polars as pl; df = pl.read_csv("data.csv"); df.lazy().filter(pl.col("age") > 18).group_by("city").agg(pl.mean("salary")).collect(). Lazy vs eager: lazy (df.lazy()...collect()) builds query plan, Polars optimizes (pushes filters down, reorders operations), then executes. Crucial for large datasets. Feature engineering in Polars: rolling windows (pl.col("price").rolling_mean(window_size=7)), lag features (pl.col("sales").shift(1)), string features (pl.col("text").str.contains("keyword")). Integration with scikit-learn: convert to numpy (df.to_numpy()) or pandas (df.to_pandas()) when needed. Scan for large files: pl.scan_csv/scan_parquet for lazy out-of-memory processing. Performance benchmarks: Polars 5-20x faster than Pandas for groupby/join operations on 100M row datasets. Memory usage 2-4x lower. Migration from Pandas: most operations have direct equivalents, main difference is explicit lazy vs eager evaluation. Use Polars for: data preprocessing in ML pipelines, feature engineering on large datasets, ETL jobs. Stick with Pandas for: interactive exploration, when Pandas-specific ecosystem needed.