We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Mathieu Cayssol & Chris Lo- We rewrote tsfresh in Polars and why you should too | PyData Global 2023
Mathieu Cayssol and Chris Lo showcase the enhanced features and performance of Polars by rewriting tsfresh, demonstrating how to leverage its capabilities for efficient feature extraction and parallelization.
- The speakers rewrites TSFRESH in Polars to take advantage of its fast execution and parallelization capabilities.
- Polars has a more expressive language than pandas and can handle complex expressions and aggregation operations.
- The reimplementation in Polars led to a significant performance improvement, especially for complex feature extraction tasks.
- Polars is faster and more memory-efficient than pandas for many tasks, including feature extraction and grouping.
- The Polars namespace is clean and provides a clear syntax for defining expressions.
- The Polars plugin system allows for extending the functionality of the library with custom plugins.
- Polars provides a powerful query engine and data frame API for handling large datasets.
- The reimplementation of TSFRESH in Polars includes optimizations for specific features and data formats.
- The library has a consistent API and is designed for high-performance computation.
- The authors share their learning experiences and tips for using Polars and rewriting libraries like TSFRESH.
- Polars has a community-driven approach and can be used with other libraries like PyData.