python-performance-optimization

Profile and optimize Python code using cProfile, memory profilers, and best practices for performance.

• Identify performance bottlenecks in Python apps
• Measure CPU and memory usage
• Improve execution speed and reduce latency
• Use profiling tools for real-time and production analysis

• CPU profiling with cProfile
• Memory tracking with memory_profiler
• Line-by-line analysis with line_profiler
• Production profiling with py-spy
• Optimize with list comprehensions and generators
• Reduce redundant computation via caching
• Parallelize I/O and CPU-heavy tasks
• Use native extensions for critical paths

1. 1.Install and run cProfile to profile CPU usage
2. 2.Use memory_profiler to track memory allocation
3. 3.Apply list comprehensions and generators for memory efficiency
4. 4.Profile production apps with py-spy in real-time
5. 5.Analyze call graphs and sort stats by cumulative time
6. 6.Validate performance improvements with timeit