Python Programming Expertise

Key Principles

• Type-annotate all public function signatures; use

  typing

  module generics and

  TypeAlias

  for clarity
• Prefer composition over inheritance; use protocols (

  typing.Protocol

  ) for structural subtyping
• Structure packages with

  pyproject.toml

  as the single source of truth for metadata, dependencies, and tool configuration
• Write tests alongside code using pytest with fixtures, parametrize, and clear arrange-act-assert structure
• Profile before optimizing; use

  cProfile

  and

  line_profiler

  to identify actual bottlenecks rather than guessing

Techniques

• Use

  dataclasses.dataclass

  for simple value objects and

  pydantic.BaseModel

  for validated data with serialization needs
• Apply

  asyncio.gather()

  for concurrent I/O tasks,

  asyncio.create_task()

  for background work, and

  async for

  with async generators
• Manage dependencies with

  uv

  for fast resolution or

  pip-compile

  for lockfile generation; pin versions in production
• Create virtual environments with

  python -m venv .venv

  or

  uv venv

  ; never install packages into the system Python
• Use context managers (

  with

  statement and

  contextlib.contextmanager

  ) for resource lifecycle management
• Apply list/dict/set comprehensions for transformations and

  itertools

  for lazy evaluation of large sequences

Common Patterns

• Repository Pattern: Abstract database access behind a protocol class with

  get()

  ,

  save()

  ,

  delete()

  methods, enabling test doubles without mocking frameworks
• Dependency Injection: Pass dependencies as constructor arguments rather than importing them at module level; this makes testing straightforward and coupling explicit
• Structured Logging: Use

  structlog

  or

  logging.config.dictConfig

  with JSON formatters for machine-parseable log output in production
• CLI with Typer: Build command-line tools with

  typer

  for automatic argument parsing from type hints, help generation, and tab completion

Pitfalls to Avoid

• Do not use mutable default arguments (

  def f(items=[])

  ); use

  None

  as default and initialize inside the function body
• Do not catch bare

  except:

  or

  except Exception

  ; catch specific exception types and let unexpected errors propagate
• Do not mix sync and async code without

  asyncio.to_thread()

  or

  loop.run_in_executor()

  for blocking operations; blocking the event loop kills concurrency
• Do not rely on import side effects for initialization; use explicit setup functions called from the application entry point