What is the difference between cProfile and line_profiler?

cProfile provides a total execution time summary for every function called, whereas line_profiler gives you a granular breakdown of exactly how much time is spent on every single line of code within a function.

How do I profile a running production Python process?

This skill provides patterns for using py-spy, which can attach to a running process ID (PID) to generate flamegraphs or top-view performance data without requiring an application restart.

Does this skill help with caching redundant operations?

Yes, it provides implementation patterns for functools.lru_cache and other caching strategies to avoid recomputing expensive function results.

Can I optimize memory usage for large datasets?

Yes, the skill includes specific implementations for generator expressions, the __slots__ attribute for classes, and NumPy vectorization to drastically reduce memory overhead.

Python Performance Optimization

Name: Python Performance Optimization
Author: Krosebrook

byKrosebrook

•

Analíticas y Monitorización

Profiles and optimizes Python code using advanced diagnostic tools and proven implementation patterns to improve execution speed and resource efficiency.

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The Python Performance Optimization skill empowers developers to systematically identify and resolve bottlenecks within their Python applications. By integrating industry-standard tools like cProfile, memory_profiler, and py-spy, it provides a comprehensive framework for CPU and memory profiling across development and production environments. Beyond simple diagnostics, the skill offers a library of high-performance coding patterns—including vectorized NumPy operations, memory-efficient generator expressions, and advanced caching strategies—to help you transform slow, resource-heavy scripts into lean, production-grade software.

Características Principales

Comprehensive CPU and memory profiling using cProfile and memory_profiler
Line-by-line execution analysis for granular bottleneck detection
Production-safe profiling with py-spy and flamegraph generation
High-performance numerical optimization via NumPy vectorization
Memory-efficient data structures using generators and class slots
1 GitHub stars

Casos de Uso

Reducing application latency and resolving high CPU utilization issues
Identifying and fixing memory leaks in long-running backend processes
Accelerating data processing pipelines and heavy numerical algorithms

Acerca de

Características Principales

Comprehensive CPU and memory profiling using cProfile and memory_profiler
Line-by-line execution analysis for granular bottleneck detection
Production-safe profiling with py-spy and flamegraph generation
High-performance numerical optimization via NumPy vectorization
Memory-efficient data structures using generators and class slots
1 GitHub stars

Casos de Uso

Reducing application latency and resolving high CPU utilization issues
Identifying and fixing memory leaks in long-running backend processes
Accelerating data processing pipelines and heavy numerical algorithms