---
name: optimizacion-rendimiento-python
description: Guía práctica para perfilar y optimizar código Python usando cProfile, memory_profiler y otras herramientas. Cubre estrategias de CPU, memoria, I/O y paralelización para eliminar cuellos de botella.
license: MIT
metadata:
  id: dd046e76
  slug: optimizacion-rendimiento-python
  titulo: "Optimización de Rendimiento en Python"
  servicio: IA-Ingenieria-MLOps
  categoria_recurso: Web-Desarrollo
  tipo: referencia
  nivel: intermedio
  idioma: es
  idioma_original: en
  acceso: gratis
  precio_eur: 0
  plataformas: [Python, cProfile, timeit, NumPy]
  dependencias: [cProfile, memory_profiler, py-spy, timeit]
  licencia: { spdx: MIT, redistribuible: true, uso_comercial: true }
  fuente:
    repo: wshobson/agents
    url: https://github.com/wshobson/agents/tree/main/plugins/python-development/skills/python-performance-optimization
    commit: cc37bfd
    autor: wshobson
    nombre_original: python-performance-optimization
    duplicados_en: []
  seguridad: { veredicto: seguro, riesgo: bajo, escaneado: "2026-06-14", motor: "grep-estatico+auditor-llm" }
  ficha:
    que_hace: "Proporciona una guía estructurada para identificar y eliminar cuellos de botella de rendimiento en código Python."
    como_lo_hace: "Aplica profiling de CPU y memoria con cProfile y py-spy, y recomienda patrones de optimización como caché, generadores y estructuras de datos adecuadas."
  content_hash: "dd046e767260951146825ad2399833a7edb50015a46b809d865b9c7a221911c0"
  version: 1.0.0
---

# 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