pytimetk 2.3.0 Release Notes

We're excited to announce the release of pytimetk 2.3.0! This update focuses on significant performance and memory optimizations, particularly for users leveraging the Polars engine. We've introduced native Polars implementations for several key functions, reducing reliance on pandas fallbacks and unlocking faster, more efficient operations on large datasets. Benchmarks show impressive gains, including up to ~7X speedups in EWM (Exponential Weighted Moving) calculations.

Key Improvements

• Polars-Native Optimizations:

  • Added dedicated Polars paths for pad_by_time, future_frame, augment_ewm, augment_rolling_apply, augment_expanding_apply, and the scalar branch of apply_by_time. This eliminates unnecessary conversions to pandas, keeping data in Arrow buffers for zero-copy chaining and reduced memory overhead.
  • For EWM operations (augment_ewm), Polars users now benefit from direct ewm_mean/std/var applications, avoiding round-trips to pandas. Benchmark results on 200k rows demonstrate ~7X faster execution (0.004s vs. 0.030s in pandas).