# 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.

Source: https://skillsagentes.com/skills/wshobson/agents/python-performance-optimization
Repository: https://github.com/wshobson/agents
Author: wshobson
License: MIT
Updated: hace 2 meses
Context cost: 49 tok installed, 813 tok once triggered, 5.3k tok with every bundled file
Bundle: 3 files, 21 KB
Permissions requested: none declared

## Install

```bash
npx -y skills add wshobson/agents --skill python-performance-optimization --agent claude-code
```

## What it does

- Perfila código Python con cProfile, memory profilers y line profiling
- Detecta cuellos de botella de CPU, memoria e I/O
- Aplica estrategias de optimización: algorítmica, caching, paralelización, extensiones nativas
- Aporta buenas prácticas y errores comunes al optimizar rendimiento en Python

## Use it when

- Identificar cuellos de botella de rendimiento en aplicaciones Python
- Reducir latencia y tiempos de respuesta
- Optimizar operaciones intensivas en CPU o reducir consumo de memoria
- Mejorar rendimiento de consultas a bases de datos o pipelines de datos

## What triggers it

- "Mi script Python va muy lento, ayúdame a perfilarlo"
- "Necesito reducir el uso de memoria de esta aplicación"
- "¿Cómo optimizo esta función que se ejecuta millones de veces?"

## Files

- SKILL.md — 3 KB
- references/advanced-patterns.md — 10 KB
- references/details.md — 7 KB

## SKILL.md

Reproduced verbatim from wshobson/agents under MIT. This section is the upstream document and is in English.

# 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. Optimization Strategies

- **Algorithmic**: Better algorithms and data structures
- **Implementation**: More efficient code patterns
- **Parallelization**: Multi-threading/processing
- **Caching**: Avoid redundant computation
- **Native Extensions**: C/Rust for critical paths

## Quick Start

### Basic Timing

```python
import time

def measure_time():
    """Simple timing measurement."""
    start = time.time()

    # Your code here
    result = sum(range(1000000))

    elapsed = time.time() - start
    print(f"Execution time: {elapsed:.4f} seconds")
    return result

# Better: use timeit for accurate measurements
import timeit

execution_time = timeit.timeit(
    "sum(range(1000000))",
    number=100
)
print(f"Average time: {execution_time/100:.6f} seconds")
```

## Detailed patterns and worked examples

Detailed pattern documentation lives in `references/details.md`. Read that file when the navigation tier above is insufficient.

## Best Practices

1. **Profile before optimizing** - Measure to find real bottlenecks
2. **Focus on hot paths** - Optimize code that runs most frequently
3. **Use appropriate data structures** - Dict for lookups, set for membership
4. **Avoid premature optimization** - Clarity first, then optimize
5. **Use built-in functions** - They're implemented in C
6. **Cache expensive computations** - Use lru_cache
7. **Batch I/O operations** - Reduce system calls
8. **Use generators** for large datasets
9. **Consider NumPy** for numerical operations
10. **Profile production code** - Use py-spy for live systems

## Common Pitfalls

- Optimizing without profiling
- Using global variables unnecessarily
- Not using appropriate data structures
- Creating unnecessary copies of data
- Not using connection pooling for databases
- Ignoring algorithmic complexity
- Over-optimizing rare code paths
- Not considering memory usage

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Skills Agentes — https://skillsagentes.com/skills/wshobson/agents/python-performance-optimization