Python Performance Optimization

About

This skill provides a comprehensive toolkit for profiling, analyzing, and optimizing Python applications to ensure peak performance. It guides users through the entire optimization lifecycle, from identifying CPU and memory bottlenecks with tools like cProfile and py-spy to implementing low-level performance patterns like generator expressions and __slots__. Whether you are debugging a slow production API, reducing the memory footprint of a data pipeline, or accelerating numerical computations with NumPy, this skill provides the domain-specific best practices and implementation patterns needed to build lightning-fast Python software.

Key Features

• Line-by-line analysis with line_profiler to pinpoint specific slow operations
• Memory usage optimization using generators, __slots__, and memory_profiler
• Computation acceleration through NumPy vectorization and functools caching
• CPU profiling with cProfile and py-spy for production monitoring
• Benchmarking patterns for comparing execution times of different algorithms
• 0 GitHub stars

Use Cases

• Reducing latency in web applications and API response times
• Optimizing data processing pipelines and large-scale mathematical computations
• Detecting and fixing memory leaks in long-running Python processes