Python Performance Optimization

Profile, analyze, and optimize Python code for better performance - CPU profiling, memory optimization, and implementation best practices.

When to Invoke

• User reports slow Python code or asks to speed up execution
• Profiling or benchmarking Python applications
• Reducing CPU time, memory consumption, or I/O wait
• Optimizing database queries or data processing pipelines
• Debugging memory leaks or excessive memory usage
• Choosing between parallelization strategies (threading, multiprocessing, async)
• Evaluating algorithmic vs implementation-level improvements

Core Concepts

Profiling Types

• CPU Profiling: Identify time-consuming functions (cProfile, py-spy)
• Memory Profiling: Track memory allocation and leaks (tracemalloc, memory_profiler)