Does it help with data science performance?

Absolutely, the skill features patterns for NumPy vectorization and efficient data processing to significantly speed up numerical computations and machine learning pipelines.

Does it support profiling in production environments?

It includes instructions for tools like py-spy, which allow for non-intrusive profiling of running Python processes without needing to restart the application.

How does this skill help identify slow Python code?

It provides standardized patterns for using cProfile and line_profiler to pinpoint exactly which functions or specific lines of code are consuming the most execution time.

Can I use this for memory leaks?

Yes, it includes specialized guidance for memory_profiler and the use of __slots__ to track and reduce memory consumption in large-scale applications.

Python Performance Optimizer

Name: Python Performance Optimizer
Author: amurata

byamurata

•

Analíticas y Monitorización

Profiles, analyzes, and optimizes Python codebases to identify performance bottlenecks and implement high-efficiency implementation patterns.

Acerca de

This skill provides a comprehensive toolkit for identifying and resolving performance issues in Python applications. It guides users through various profiling techniques, including CPU, memory, and line-by-line analysis using industry-standard tools like cProfile, memory-profiler, and py-spy. Beyond diagnosis, it offers actionable implementation patterns for optimization, ranging from efficient data structure selection and caching strategies to advanced techniques like NumPy vectorization and memory-efficient slotted classes, making it essential for scaling Python services and data pipelines.

Características Principales

Line-by-line performance analysis patterns for pinpointing bottlenecks
Advanced optimization blueprints including NumPy and caching
Memory management strategies to eliminate leaks and reduce footprint
Comprehensive CPU and memory profiling integration
Production-safe profiling techniques for running processes
3 GitHub stars

Casos de Uso

Reducing memory leaks and high RAM usage in data processing pipelines
Identifying slow functions and CPU bottlenecks in complex backend services
Implementing high-performance algorithms for latency-critical applications

Acerca de

Características Principales

Line-by-line performance analysis patterns for pinpointing bottlenecks
Advanced optimization blueprints including NumPy and caching
Memory management strategies to eliminate leaks and reduce footprint
Comprehensive CPU and memory profiling integration
Production-safe profiling techniques for running processes
3 GitHub stars

Casos de Uso

Reducing memory leaks and high RAM usage in data processing pipelines
Identifying slow functions and CPU bottlenecks in complex backend services
Implementing high-performance algorithms for latency-critical applications