Aaronbassett Paneful Python Core

Python Core Development

Comprehensive guidance for modern Python development with focus on FastAPI, type safety, and best practices.

Quick Reference Guide

By Task Type

Getting Started

• New Project: Use scripts/init_python_project.sh for FastAPI or package projects
• Core Principles: See references/principles.md for PEP 8 and Python philosophy
• Common Errors: See references/common-errors.md for solutions

Writing Code

• Type Hints: See references/type-hints.md for annotations and mypy
• Async Programming: See references/async-patterns.md for asyncio patterns
• Testing: See references/testing.md for pytest strategies

Web Development (FastAPI)

• FastAPI Guide: See references/fastapi-guide.md for comprehensive tutorial
• Essential Libraries: See references/common-libraries.md

Code Quality

• Code Review: See references/code-review.md for checklist
• Performance: See references/performance.md for optimization

Project Management

• Dependencies: See references/dependencies.md for pip, poetry, uv
• Project Structure: See references/project-structure.md

By Question Type

Core Workflows

1. Starting a FastAPI Project

1. Initialize Project

   ./scripts/init_python_project.sh my-api fastapi
   cd my-api
   

2. Set Up Environment

   python -m venv venv
   source venv/bin/activate  # or `venv\Scripts\activate` on Windows
   pip install fastapi uvicorn[standard] sqlalchemy pydantic
   pip install pytest mypy ruff --dev
   

3. Configure Tools

   • Copy assets/configs/ruff.toml for linting
   • Copy assets/configs/mypy.ini for type checking
   • Copy assets/configs/pytest.ini for testing

4. Start Development

   uvicorn app.main:app --reload
   

   Visit http://localhost:8000/docs for automatic API documentation

2. Building a FastAPI Endpoint

1. Define Pydantic Models

   from pydantic import BaseModel, EmailStr
   
   class UserCreate(BaseModel):
       username: str
       email: EmailStr
       password: str
   
   class User(BaseModel):
       id: int
       username: str
       email: EmailStr
       is_active: bool = True
   

2. Create Endpoint

   from fastapi import FastAPI, Depends, HTTPException
   from sqlalchemy.orm import Session
   
   app = FastAPI()
   
   @app.post("/users/", response_model=User)
   async def create_user(
       user: UserCreate,
       db: Session = Depends(get_db)
   ):
       db_user = crud.create_user(db, user)
       return db_user
   

3. Add Tests

   from fastapi.testclient import TestClient
   
   def test_create_user():
       response = client.post(
           "/users/",
           json={"username": "test", "email": "test@example.com", "password": "secret"}
       )
       assert response.status_code == 200
       assert response.json()["username"] == "test"
   

3. Code Quality Workflow

1. Type Check

   mypy app/
   

2. Lint and Format

   ruff check app/
   ruff format app/
   

3. Run Tests

   pytest --cov=app
   

4. Security Audit

   ./scripts/audit_dependencies.sh
   

Decision Guides

When to Use FastAPI vs Django vs Flask

Use FastAPI when:

• Building modern REST APIs
• Need automatic OpenAPI/Swagger docs
• Want async/await support
• Type safety is important
• Performance is critical

Use Django when:

• Building full-stack web applications
• Need admin interface out of the box
• Want ORM with migrations
• Building monolithic applications

Use Flask when:

• Need maximum flexibility
• Building small to medium APIs
• Want lightweight framework
• Learning web development

See fastapi-guide.md for comprehensive FastAPI patterns.

Type Hints Strategy

Always use type hints for:

• Public function signatures
• Class attributes
• Function return types
• Complex data structures

Example:

from typing import Optional

def process_data(
    items: list[str],
    filter_fn: Optional[callable] = None
) -> dict[str, int]:
    """Process items and return counts."""
    return {item: len(item) for item in items}

See type-hints.md for advanced patterns.

Async vs Sync

Use async when:

• I/O-bound operations (HTTP requests, database queries)
• Need high concurrency
• Using FastAPI (built for async)
• Working with async libraries (httpx, asyncpg)

Use sync when:

• CPU-bound operations
• Simple scripts
• Libraries don't support async
• Complexity isn't justified

See async-patterns.md for asyncio patterns.

Automation Scripts

scripts/init_python_project.sh

Initialize a new Python project with best practices:

• FastAPI or package structure
• pyproject.toml with modern config
• Development dependencies (pytest, mypy, ruff)
• Proper .gitignore

Usage: ./scripts/init_python_project.sh my-project [package|fastapi]

scripts/audit_dependencies.sh

Audit dependencies for security vulnerabilities:

• Runs pip-audit for known CVEs
• Checks for outdated packages

Usage: ./scripts/audit_dependencies.sh

scripts/setup_logging.sh

Set up structured logging with structlog:

• Installs structlog
• Creates configuration file
• JSON logging for production

Usage: ./scripts/setup_logging.sh

Configuration Templates

assets/configs/ruff.toml

Modern Python linter and formatter configuration:

• 100 character line length
• Comprehensive rule selection
• Import sorting

assets/configs/mypy.ini

Static type checker configuration:

• Strict mode enabled
• Python 3.11+ features
• Comprehensive warnings

assets/configs/pytest.ini

Testing framework configuration:

• Coverage reporting
• Async test support
• HTML coverage reports

assets/configs/pyproject.toml

Complete project configuration template:

• FastAPI dependencies
• Development tools
• Tool configurations

Reference Documentation

Core Python

• principles.md - PEP 8, Zen of Python, best practices
• type-hints.md - Type annotations, mypy, protocols
• async-patterns.md - asyncio, async/await, concurrency

Development

• testing.md - pytest, fixtures, mocking, coverage
• project-structure.md - Package layout, src/ pattern
• dependencies.md - pip, poetry, uv, pyproject.toml
• performance.md - Profiling, optimization strategies
• code-review.md - Review checklist, anti-patterns

Web Development

• fastapi-guide.md - Comprehensive FastAPI tutorial
• common-libraries.md - Essential Python packages

Troubleshooting

• common-errors.md - Common mistakes and solutions

FastAPI Quick Start

# main.py
from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI(title="My API")

class Item(BaseModel):
    name: str
    price: float
    is_offer: bool = False

@app.get("/")
async def root():
    return {"message": "Hello World"}

@app.post("/items/")
async def create_item(item: Item):
    return {"item_name": item.name, "item_price": item.price}

@app.get("/items/{item_id}")
async def read_item(item_id: int, q: str | None = None):
    return {"item_id": item_id, "q": q}

Run: uvicorn main:app --reload

Docs: http://localhost:8000/docs

Best Practices Summary

1. Use type hints everywhere - Enable mypy strict mode
2. Follow PEP 8 - Use ruff for linting and formatting
3. Write tests - Aim for 80%+ coverage with pytest
4. Use async for I/O - FastAPI is built for async
5. Validate with Pydantic - Type-safe data validation
6. Structure projects properly - Use src/ layout
7. Document code - Docstrings and type hints
8. Handle errors explicitly - Specific exception types
9. Audit dependencies - Regular security checks
10. Profile before optimizing - Measure, don't guess

Example: Complete FastAPI Application

See fastapi-guide.md for:

• Database integration with SQLAlchemy
• Authentication with JWT
• File uploads
• WebSocket support
• Background tasks
• Testing strategies
• Deployment patterns
• Project structure

When to Consult References

Load references progressively:

1. Starting out: Read principles.md for Python fundamentals
2. Building web API: Consult fastapi-guide.md
3. Adding types: Reference type-hints.md
4. Going async: See async-patterns.md
5. Testing: Use testing.md
6. Debugging: Check common-errors.md
7. Reviewing: Use code-review.md checklist
8. Optimizing: See performance.md

Don't load all references at once—consult them as needs arise.

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