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python-performance-optimization

Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.

Python Performance Optimization

Comprehensive guide to profiling, analyzing, and optimizing Python code for better performance, including CPU profiling, memory optimization, and implementation best practices.

When to Use This Skill

Identifying performance bottlenecks in Python applications
Reducing application latency and response times
Optimizing CPU-intensive operations
Reducing memory consumption and memory leaks
Improving database query performance
Optimizing I/O operations
Speeding up data processing pipelines
Implementing high-performance algorithms
Profiling production applications

Core Concepts

1. Profiling Types

CPU Profiling: Identify time-consuming functions
Memory Profiling: Track memory allocation and leaks
Line Profiling: Profile at line-by-line granularity
Call Graph: Visualize function call relationships

2. Performance Metrics

Execution Time: How long operations take
Memory Usage: Peak and average memory consumption
CPU Utilization: Processor usage patterns
I/O Wait: Time spent on I/O operations

3 files · 21 KB3 KB

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npx skilletmd add @wshobson/python-performance-optimization -y

Version history

Python Performance Optimization

Python Performance Optimization

When to Use This Skill

Core Concepts

1. Profiling Types

2. Performance Metrics

3. Optimization Strategies

Quick Start

Basic Timing

Detailed patterns and worked examples

Best Practices

Common Pitfalls