Python Developer Resume Keywords

What You Need to Know

Python developers appreciate the language's readability but face its limitations daily. The Global Interpreter Lock means CPU-bound code won't benefit from multiple threads, forcing you to use multiprocessing or async approaches instead. Dynamic typing catches errors at runtime rather than compile time, making comprehensive testing essential. Virtual environments prevent dependency conflicts between projects but require discipline to manage properly. Django provides everything you need out of the box while Flask requires assembling components yourself—both approaches have merits depending on your needs. Package management with pip works until version conflicts arise, then tools like Poetry become necessary. Python's popularity for beginners doesn't mean professional Python development is simple. Production Python requires discipline around testing, error handling, logging, and performance optimization that tutorials rarely teach. Python has become one of the most popular programming languages, regularly appearing at the top of language rankings. Its readability and relatively gentle learning curve make it a common first programming language. But professional Python development differs significantly from writing scripts or following tutorials. Production code needs robust error handling with try-except blocks, structured logging with the logging module rather than print statements, comprehensive testing with pytest, and performance optimization when necessary. Understanding Python's memory model, garbage collection through reference counting, and the Global Interpreter Lock helps avoid subtle bugs and performance problems. Django is Python's most comprehensive web framework, providing a "batteries included" approach. Its ORM abstracts SQL, the admin interface provides automatic CRUD interfaces, the authentication system handles users and permissions, and the template engine renders HTML. This makes Django incredibly productive for building complete web applications quickly. But Django's comprehensiveness means learning Django-specific concepts—models, views, templates, middleware, signals, and the URL configuration system. Django's ORM is powerful but can generate inefficient queries. Understanding select_related and prefetch_related is essential for avoiding N+1 query problems. Django Rest Framework builds APIs on Django, providing serializers that convert between Python objects and JSON, viewsets that combine common API patterns, and routers that generate URL patterns automatically. Flask takes the opposite approach—it provides a minimal core and lets you add what you need. This flexibility appeals to developers who want control over their stack, but it means making more architectural decisions. Should you use SQLAlchemy or raw SQL? Which template engine—Jinja2, Mako, or something else? How will you handle authentication—Flask-Login, Flask-Security, or custom code? Flask is lighter than Django, making it suitable for microservices and APIs that don't need Django's full feature set. But building a complex application requires more code because you're assembling pieces yourself rather than using Django's integrated approach. FastAPI represents the modern alternative to Django and Flask. Built on Starlette for async support and Pydantic for data validation, FastAPI offers excellent performance and automatic API documentation generation through OpenAPI. Type hints become validation rules, catching errors during development. Async support enables high concurrency for I/O-bound workloads. But async programming requires understanding coroutines, event loops, and async/await syntax. Converting existing synchronous code to async isn't trivial—database queries, file operations, and HTTP requests all need async alternatives. Not all libraries support async, limiting choices. Data processing is where Python truly shines. Libraries like pandas manipulate tabular data with powerful operations, but pandas has hundreds of methods with subtle differences. Understanding when to use apply, map, or vectorized operations affects both code clarity and performance. NumPy provides efficient array operations fundamental to scientific computing, but array broadcasting rules take time to internalize. Scikit-learn implements machine learning algorithms with consistent APIs, but understanding which algorithm suits your problem requires both theory and experimentation. Web scraping with BeautifulSoup parses HTML, while Scrapy provides a complete framework for building spiders. Handling dynamic content requires Selenium or Playwright to control browsers. Testing Python properly requires pytest for most projects, though unittest remains in the standard library. Pytest provides better assertion introspection, fixture management, and plugin ecosystem. But writing good tests requires discipline—testing edge cases, mocking external dependencies with unittest.mock, and achieving reasonable coverage takes time. Type hints with mypy catch errors that dynamic typing misses, essentially adding compile-time type checking to Python. Adding types to existing code is tedious but valuable, preventing entire classes of bugs. Understanding protocols, generics, and type narrowing helps write better typed Python code. Deployment and packaging have improved but remain complex. Creating requirements.txt files lists dependencies simply but doesn't handle version conflicts. Pipenv and Poetry provide better dependency management with lock files ensuring reproducible builds. Packaging applications with setuptools or modern tools like Flit enables distribution through PyPI. Docker containers simplify deployment by bundling Python, dependencies, and application code into a consistent environment. Understanding the difference between development, staging, and production environments and how to configure each appropriately is essential for reliable deployments.