Python

Modern Python development with type hints, async/await, and best practices.

When to Use

• Working with .py files
• Building APIs with FastAPI/Django
• Data analysis with pandas/numpy
• Scripting and automation

Quick Start

from typing import Optional, List

def process_items(
    items: list[str],
    transform: Optional[callable] = None,
) -> list[str]:
    if transform:
        return [transform(item) for item in items]
    return items

Core Concepts

Type Hints

from typing import TypeVar, Generic
from collections.abc import Callable, Iterator

def process_items(
    items: list[str],
    transform: Callable[[str], str] | None = None,
) -> list[str]:
    if transform:
        return [transform(item) for item in items]
    return items

# Use TypeVar for generics
T = TypeVar('T')

def first(items: list[T]) -> T | None:
    return items[0] if items else None

Dataclasses & Pydantic

from dataclasses import dataclass, field
from pydantic import BaseModel, Field

# Dataclass for simple data containers
@dataclass
class User:
    name: str
    email: str
    tags: list[str] = field(default_factory=list)

# Pydantic for validation
class UserCreate(BaseModel):
    name: str = Field(..., min_length=1, max_length=100)
    email: str = Field(..., pattern=r'^[\w\.-]+@[\w\.-]+\.\w+$')

Common Patterns

Async Patterns

import asyncio

async def fetch_all(urls: list[str]) -> list[Response]:
    async with aiohttp.ClientSession() as session:
        tasks = [fetch_one(session, url) for url in urls]
        return await asyncio.gather(*tasks)

# Use TaskGroup for structured concurrency (3.11+)
async def process_batch(items: list[Item]) -> list[Result]:
    results = []
    async with asyncio.TaskGroup() as tg:
        for item in items:
            tg.create_task(process_item(item, results))
    return results

Context Managers

from contextlib import contextmanager, asynccontextmanager

@contextmanager
def managed_resource() -> Iterator[Resource]:
    resource = Resource()
    try:
        yield resource
    finally:
        resource.cleanup()

Best Practices

Do:

• Use type hints for function signatures
• Use pyproject.toml for project configuration
• Use virtual environments (venv, poetry)
• Use generators for large datasets

Don’t:

• Use mutable default arguments (def f(x=[])
• Use import * (pollutes namespace)
• Catch bare except: (catch specific exceptions)
• Use assert for input validation

Troubleshooting

References

• Python Official Docs
• Real Python