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Explore comprehensive performance optimization techniques for Python code through a real-world case study of polygon triangulation in this 46-minute conference talk from EuroPython 2025. Learn systematic approaches to identifying and resolving performance bottlenecks when your code works correctly but runs too slowly, with particular focus on shape-handling algorithms and interactive GUI applications. Discover how to profile code effectively to pinpoint performance issues, then progress through multiple optimization strategies including algorithm complexity reduction, NumPy optimizations, and just-in-time compilation using Numba. Examine when and how to implement compiled extensions using Cython, C++, and Rust, with practical insights into the strengths and limitations of each approach. Understand the unique challenges of optimizing performance for interactive applications through a detailed case study of reducing latency in the napari GUI framework. Gain practical knowledge about profiling techniques, algorithm improvements, compilation strategies, and the decision-making process for choosing between different optimization approaches, making this essential viewing for Python developers working on numerical computing, scientific applications, or any performance-critical interactive software.
