Python

Python rewards fluency in small details. A line that looks obvious can mean “create a generator,” “schedule a coroutine but do not run it yet,” “reuse an object,” “build a new list eagerly,” or “stand in for missing code.” This section focuses on those language features and idioms: the pieces that sit below a framework and above raw computer science.

The goal is not to catalog every keyword. The useful target is judgment. Know what a construct means at runtime, what it costs, when it makes code clearer, and when it hides control flow or allocation.

Topics

• The Ellipsis (...), the built-in singleton and its four roles: placeholder body, type hint argument, NumPy slice, and stub marker
• List Comprehensions, the four forms (list, dict, set, generator), when to use each, and where they become a liability
• Async in Python, coroutines, the event loop, tasks, and asyncio.gather, plus when async helps and when it doesn’t

How to use this section

Use these pages when Python itself is part of the problem. If the question is “which data structure solves this interview problem,” start with Computer Science. If the question is “how do I build a Django app,” start with Django. If the question is “why did this Python program behave differently than I expected,” this section is the right place.

Three recurring questions show up across the language:

• What gets created? A list comprehension creates a list immediately. A generator expression creates a lazy iterator. ... creates the singleton Ellipsis. An async def call creates a coroutine object before anything inside the function runs.
• When does work happen? Normal function calls run now. Coroutines run when awaited or scheduled. Generators advance only when consumed. Comprehensions evaluate their clauses in a specific order.
• What convention is the reader expecting? pass, ..., list comprehensions, generator expressions, and async helpers all carry social meaning in Python code, not just runtime behavior.

How the topics connect

Language topics link outward to wherever the feature shows up in practice. List comprehensions and generators matter in data transformation and coding-problem solutions. Async matters in web handlers, background jobs, crawlers, and clients that wait on many network calls. Ellipsis appears in abstract methods, type stubs, scientific indexing, and placeholder bodies.

Python feature
  |
  +-- runtime meaning: allocation, laziness, scheduling, mutation
  +-- code-reading meaning: convention, intent, maintainability
  +-- application surface: web, testing, data work, coding problems

What to watch for

• Convenience can hide cost: a one-line comprehension may allocate a full intermediate list. Use a generator when streaming behavior matters.
• Async is concurrency, not speed by default: it helps when tasks wait on I/O. It does not make CPU-bound Python loops faster.
• Placeholders communicate intent: pass says an empty body is intentional. ... often says “shape exists, body omitted.”
• Idioms age into contracts: readers expect Pythonic code to use the standard shape. Clever alternatives need to buy enough clarity or performance to justify themselves.

Related topics

• Computer Science, data structures, algorithms, and problem-solving patterns.
• Django, Python web development from project setup through production.
• Testing, where Python idioms show up in fixtures, stubs, and behavior-focused tests.
• Coding problems, algorithm practice where Python’s collections and iteration model matter.