Python

Universal Principles

• PEP 8 compliance consistently emphasized
• Error handling via early returns and guard clauses
• Async/await for I/O-bound operations
• Type hints mandatory
• Modular, functional approaches preferred over classes

Code Style

• Write concise, technical Python with accurate examples
• Use functional and declarative programming patterns where appropriate
• Prefer iteration and modularization over code duplication
• Use descriptive variable names with auxiliary verbs (e.g.,

  is_active

  ,

  has_permission

  )
• Use lowercase with underscores for file/directory naming

Data Analysis

• Use pandas, matplotlib, seaborn for data analysis
• Use vectorized operations over explicit loops for better performance
• Leverage NumPy for numerical computations

Web Development

Django

• Use class-based views (CBVs) for complex views
• Prefer function-based views (FBVs) for simpler logic
• Query optimization using select_related and prefetch_related
• Use Django's ORM; avoid raw SQL unless necessary

FastAPI

• Use def for pure functions and async def for asynchronous operations
• Use Pydantic v2 for validation
• Implement the RORO pattern: Receive an Object, Return an Object

Flask

• Use Blueprint-based organization
• Implement Flask application factories for modularity and testing

Error Handling

• Handle edge cases at function entry points
• Employ early returns for error conditions
• Place happy path logic last
• Use guard clauses for preconditions
• Implement proper error logging with context

Performance

• Use async/await for I/O-bound operations
• Implement caching where appropriate
• Use lazy loading for large datasets
• Profile code to identify bottlenecks