- Compatibility with pandas-flavor 0.8.0
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This release introduces significant enhancements to visualization capabilities, integrates tidy selectors for improved column handling, completes the migration to Ray for parallelism, and includes various performance optimizations and bug fixes. Key themes include better diagnostics plotting, more flexible data manipulation, and improved documentation...
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. Benc...
alpha values to compute multiple EWM columns in one call. Improved Polars integration and documentation..tk Accessor for LazyFrames: Added a .tk accessor for pl.LazyFrame objects, enabling direct access to pytimetk helpers (e.g., df.lazy().tk.augment_rolling(...)) with performance parity to pandas and Polars DataFrame paths. This enhances support for lazy evaluation pipelines, improving efficiency for large datasets.