python-performance

# Python Performance Optimization Profiling and optimization patterns for Python code. ## Table of Contents 1. [Quick Start](#quick-start) ## Quick Start ```python # Basic timing import timeit time = timeit.timeit("sum(range(1000000))", number=100) print(f"Average: {time/100:.6f}s") ``` **Verification:** Run the command with `--help` flag to verify availability. ## When To Use - Identifying performance bottlenecks - Reducing application latency - Optimizing CPU-intensive operations - Reducing memory consumption - Profiling production applications - Improving database query performance ## When NOT To Use - Async concurrency - use python-async instead - CPU/GPU system monitoring - use conservation:cpu-gpu-performance - Async concurrency - use python-async instead - CPU/GPU system monitoring - use conservation:cpu-gpu-performance ## Modules This skill is organized into focused modules for progressive loading: ### [profiling-tools](modules/profiling-tools.md) CPU profiling with cProfile, line profiling, memory profiling, and production profiling with py-spy. Essential for identifying where your code spends time and memory. ### [optimization-patterns](modules/optimization-patterns.md) Ten proven optimization patterns including list comprehensions, generators, caching, string concatenation, data structures, NumPy, multiprocessing, and database operations. ### [memory-management](modules/memory-management.md) Memory optimization techniques including leak tracking w...