Python开发指南：现代Python 3.10+特性、最佳实践与性能优化

python-development by laurigates/claude-plugins

106 周安装量

19 GitHub Stars

GitHub

安装命令

npx skills add https://github.com/laurigates/claude-plugins --skill python-development

Python Web框架开发调试

🇨🇳中文介绍

Python 开发

Python 核心语言概念、惯用法和最佳实践。

核心专长

Python 语言 : 现代 Python 3.10+ 特性与惯用法
最佳实践 : Pythonic 代码、设计模式、SOLID 原则
调试 : 交互式调试与性能分析技术
性能 : 优化策略与性能分析
异步编程 : async/await 模式与 asyncio

现代 Python 特性 (3.10+)

类型提示

# Modern syntax (Python 3.10+)
def process_items(
    items: list[str],                    # Not List[str]
    mapping: dict[str, int],             # Not Dict[str, int]
    optional: str | None = None,         # Not Optional[str]
) -> tuple[bool, str]:                   # Not Tuple[bool, str]
    """使用现代类型提示处理项目。"""
    return True, "success"

# Type aliases
type UserId = int
type UserDict = dict[str, str | int]

def get_user(user_id: UserId) -> UserDict:
    return {"id": user_id, "name": "Alice"}

模式匹配 (3.10+)

def handle_command(command: dict) -> str:
    match command:
        case {"action": "create", "item": item}:
            return f"Creating {item}"
        case {"action": "delete", "item": item}:
            return f"Deleting {item}"
        case {"action": "list"}:
            return "Listing items"
        case _:
            return "Unknown command"

广告位招租

在这里展示您的产品或服务

触达数万 AI 开发者，精准高效

联系我们

迭代器与生成器

def fibonacci():
    a, b = 0, 1
    while True:
        yield a
        a, b = b, a + b

# 使用生成器
fib = fibonacci()
first_ten = [next(fib) for _ in range(10)]

# 生成器表达式
even_squares = (x**2 for x in range(10) if x % 2 == 0)

import pdb

def problematic_function():
    value = calculate()
    pdb.set_trace()  # 调试器断点
    return process(value)



# 出错时调试
python -m pdb script.py

# 使用调试器的 pytest
uv run pytest --pdb                     # 失败时进入 pdb
uv run pytest --pdb --pdbcls=IPython.terminal.debugger:TerminalPdb

# CPU 性能分析
uv run python -m cProfile -s cumtime script.py | head -20

# 逐行性能分析（临时依赖）
uv run --with line-profiler kernprof -l -v script.py

# 内存分析（临时依赖）
uv run --with memory-profiler python -m memory_profiler script.py

# 实时性能分析（临时工具）
uvx py-spy top -- python script.py

# 使用 scalene 快速分析
uv run --with scalene python -m scalene script.py

# 跟踪执行
import sys

def trace_calls(frame, event, arg):
    if event == 'call':
        print(f"Calling {frame.f_code.co_name}")
    return trace_calls

sys.settrace(trace_calls)

# 内存跟踪
import tracemalloc

tracemalloc.start()
# ... 要分析的代码
snapshot = tracemalloc.take_snapshot()
top_stats = snapshot.statistics('lineno')
for stat in top_stats[:10]:
    print(stat)

import asyncio

async def fetch_data(url: str) -> dict:
    async with httpx.AsyncClient() as client:
        response = await client.get(url)
        return response.json()

async def main():
    result = await fetch_data("https://api.example.com")
    print(result)

asyncio.run(main())

async def process_multiple():
    # 并发运行
    results = await asyncio.gather(
        fetch_data("url1"),
        fetch_data("url2"),
        fetch_data("url3"),
    )
    return results

# 带超时
async def with_timeout():
    try:
        result = await asyncio.wait_for(fetch_data("url"), timeout=5.0)
    except asyncio.TimeoutError:
        print("请求超时")

from typing import Protocol

class Database(Protocol):
    def query(self, sql: str) -> list: ...

def get_users(db: Database) -> list:
    return db.query("SELECT * FROM users")

def create_handler(handler_type: str):
    match handler_type:
        case "json":
            return JSONHandler()
        case "xml":
            return XMLHandler()
        case _:
            raise ValueError(f"未知处理器: {handler_type}")

from functools import wraps
import time

def timer(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        start = time.time()
        result = func(*args, **kwargs)
        end = time.time()
        print(f"{func.__name__} 耗时 {end - start:.2f}s")
        return result
    return wrapper

@timer
def slow_function():
    time.sleep(1)

单一职责原则:

# 不好：类承担了太多职责
class User:
    def save(self): pass
    def send_email(self): pass
    def generate_report(self): pass

# 好：职责分离
class User:
    def save(self): pass

class EmailService:
    def send_email(self, user): pass

class ReportGenerator:
    def generate(self, user): pass

def process_data(data: dict) -> str:
    # 尽早验证
    if not data:
        raise ValueError("数据不能为空")
    if "required_field" not in data:
        raise KeyError("缺少必填字段")

    # 放心处理
    return data["required_field"].upper()

# 优先使用不可变转换
def process_items(items: list[int]) -> list[int]:
    return [item * 2 for item in items]  # 新列表

# 而不是修改原数据
def process_items_bad(items: list[int]) -> None:
    for i in range(len(items)):
        items[i] *= 2  # 修改输入

项目结构 (src 布局)

my-project/
├── pyproject.toml
├── README.md
├── src/
│   └── my_project/
│       ├── __init__.py
│       ├── core.py
│       ├── utils.py
│       └── models.py
└── tests/
    ├── conftest.py
    ├── test_core.py
    └── test_utils.py

uv-run - 运行脚本、临时依赖项、PEP 723
uv-project-management - 项目设置与依赖管理
uv-tool-management - 全局安装 CLI 工具
python-testing - 使用 pytest 进行测试
python-code-quality - 使用 ruff/ty 进行代码检查和类型检查
python-packaging - 构建和发布包
uv-python-versions - 管理 Python 解释器

🇺🇸English

Python Development

Core Python language concepts, idioms, and best practices.

Core Expertise

Python Language : Modern Python 3.10+ features and idioms
Best Practices : Pythonic code, design patterns, SOLID principles
Debugging : Interactive debugging and profiling techniques
Performance : Optimization strategies and profiling
Async Programming : async/await patterns and asyncio

Modern Python Features (3.10+)

Type Hints

# Modern syntax (Python 3.10+)
def process_items(
    items: list[str],                    # Not List[str]
    mapping: dict[str, int],             # Not Dict[str, int]
    optional: str | None = None,         # Not Optional[str]
) -> tuple[bool, str]:                   # Not Tuple[bool, str]
    """Process items with modern type hints."""
    return True, "success"

# Type aliases
type UserId = int
type UserDict = dict[str, str | int]

def get_user(user_id: UserId) -> UserDict:
    return {"id": user_id, "name": "Alice"}

Pattern Matching (3.10+)

def handle_command(command: dict) -> str:
    match command:
        case {"action": "create", "item": item}:
            return f"Creating {item}"
        case {"action": "delete", "item": item}:
            return f"Deleting {item}"
        case {"action": "list"}:
            return "Listing items"
        case _:
            return "Unknown command"

Structural Pattern Matching

def process_response(response):
    match response:
        case {"status": 200, "data": data}:
            return process_success(data)
        case {"status": 404}:
            raise NotFoundError()
        case {"status": code} if code >= 500:
            raise ServerError(code)

Python Idioms

Context Managers

# File handling
with open("file.txt") as f:
    content = f.read()

# Custom context manager
from contextlib import contextmanager

@contextmanager
def database_connection():
    conn = create_connection()
    try:
        yield conn
    finally:
        conn.close()

with database_connection() as conn:
    conn.execute("SELECT * FROM users")

List Comprehensions

# List comprehension
squares = [x**2 for x in range(10)]

# Dict comprehension
word_lengths = {word: len(word) for word in ["hello", "world"]}

# Set comprehension
unique_lengths = {len(word) for word in ["hello", "world", "hi"]}

# Generator expression
sum_of_squares = sum(x**2 for x in range(1000000))  # Memory efficient

Iterators and Generators

def fibonacci():
    a, b = 0, 1
    while True:
        yield a
        a, b = b, a + b

# Use generator
fib = fibonacci()
first_ten = [next(fib) for _ in range(10)]

# Generator expression
even_squares = (x**2 for x in range(10) if x % 2 == 0)

Debugging

Interactive Debugging

import pdb

def problematic_function():
    value = calculate()
    pdb.set_trace()  # Debugger breakpoint
    return process(value)



# Debug on error
python -m pdb script.py

# pytest with debugger
uv run pytest --pdb                     # Drop into pdb on failure
uv run pytest --pdb --pdbcls=IPython.terminal.debugger:TerminalPdb

Performance Profiling

# CPU profiling
uv run python -m cProfile -s cumtime script.py | head -20

# Line-by-line profiling (temporary dependency)
uv run --with line-profiler kernprof -l -v script.py

# Memory profiling (temporary dependency)
uv run --with memory-profiler python -m memory_profiler script.py

# Real-time profiling (ephemeral tool)
uvx py-spy top -- python script.py

# Quick profiling with scalene
uv run --with scalene python -m scalene script.py

Built-in Debugging Tools

# Trace execution
import sys

def trace_calls(frame, event, arg):
    if event == 'call':
        print(f"Calling {frame.f_code.co_name}")
    return trace_calls

sys.settrace(trace_calls)

# Memory tracking
import tracemalloc

tracemalloc.start()
# ... code to profile
snapshot = tracemalloc.take_snapshot()
top_stats = snapshot.statistics('lineno')
for stat in top_stats[:10]:
    print(stat)

Async Programming

Basic async/await

import asyncio

async def fetch_data(url: str) -> dict:
    async with httpx.AsyncClient() as client:
        response = await client.get(url)
        return response.json()

async def main():
    result = await fetch_data("https://api.example.com")
    print(result)

asyncio.run(main())

Concurrent Tasks

async def process_multiple():
    # Run concurrently
    results = await asyncio.gather(
        fetch_data("url1"),
        fetch_data("url2"),
        fetch_data("url3"),
    )
    return results

# With timeout
async def with_timeout():
    try:
        result = await asyncio.wait_for(fetch_data("url"), timeout=5.0)
    except asyncio.TimeoutError:
        print("Request timed out")

Design Patterns

Dependency Injection

from typing import Protocol

class Database(Protocol):
    def query(self, sql: str) -> list: ...

def get_users(db: Database) -> list:
    return db.query("SELECT * FROM users")

Factory Pattern

def create_handler(handler_type: str):
    match handler_type:
        case "json":
            return JSONHandler()
        case "xml":
            return XMLHandler()
        case _:
            raise ValueError(f"Unknown handler: {handler_type}")

Decorator Pattern

from functools import wraps
import time

def timer(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        start = time.time()
        result = func(*args, **kwargs)
        end = time.time()
        print(f"{func.__name__} took {end - start:.2f}s")
        return result
    return wrapper

@timer
def slow_function():
    time.sleep(1)

Best Practices

SOLID Principles

Single Responsibility:

# Bad: Class does too much
class User:
    def save(self): pass
    def send_email(self): pass
    def generate_report(self): pass

# Good: Separate concerns
class User:
    def save(self): pass

class EmailService:
    def send_email(self, user): pass

class ReportGenerator:
    def generate(self, user): pass

Fail Fast

def process_data(data: dict) -> str:
    # Validate early
    if not data:
        raise ValueError("Data cannot be empty")
    if "required_field" not in data:
        raise KeyError("Missing required field")

    # Process with confidence
    return data["required_field"].upper()

Functional Approach

# Prefer immutable transformations
def process_items(items: list[int]) -> list[int]:
    return [item * 2 for item in items]  # New list

# Over mutations
def process_items_bad(items: list[int]) -> None:
    for i in range(len(items)):
        items[i] *= 2  # Mutates input

Project Structure (src layout)

my-project/
├── pyproject.toml
├── README.md
├── src/
│   └── my_project/
│       ├── __init__.py
│       ├── core.py
│       ├── utils.py
│       └── models.py
└── tests/
    ├── conftest.py
    ├── test_core.py
    └── test_utils.py

References

Python docs: https://docs.python.org/3/
Type hints: https://docs.python.org/3/library/typing.html
Async: https://docs.python.org/3/library/asyncio.html
Detailed guide: See REFERENCE.md

Weekly Installs

106

Repository

laurigates/clau…-plugins

GitHub Stars

First Seen

Jan 29, 2026

Security Audits

Gen Agent Trust HubPass SocketPass SnykPass

Installed on

opencode103

github-copilot102

codex101

gemini-cli100

kimi-cli100

amp100

agent-browser 浏览器自动化工具 - Vercel Labs 命令行网页操作与测试

157,400 周安装

Python开发指南：现代Python 3.10+特性、最佳实践与性能优化

🇨🇳中文介绍

Python 开发

核心专长

现代 Python 特性 (3.10+)

类型提示

模式匹配 (3.10+)

相关 Skills

结构模式匹配

Python 惯用法

上下文管理器

列表推导式

迭代器与生成器

调试

交互式调试

性能分析

内置调试工具

异步编程

基础 async/await

并发任务

设计模式

依赖注入

工厂模式

装饰器模式

最佳实践

SOLID 原则

快速失败

函数式方法