I have about 30 years of programming experience, mainly with C# and C++, and I have only experimented with Python so far. With the growth of AI, I want to become more comfortable with Python so I can work with tools such as LangGraph. I already understand many AI concepts because I have built custom solutions and used systems such as Stateless and Temporal, but I have never started a Python project from scratch with a solid development workflow and CI/CD pipeline.
Tools like Cursor can generate a surprising amount of code, but I am not comfortable relying on generated code without understanding how it works. I would like to learn Python properly and avoid simply applying C# or C++ habits to it. What should an experienced developer focus on when learning Python, including language features, best practices, project structure, testing, dependency management, and CI/CD?
4 Answers
The transition should be fairly smooth. Python is a higher-level language with less ceremony than C# or C++, so you can become productive quickly, although mastering its idioms and ecosystem still takes time. Use a Python-focused editor or IDE with good debugging, testing, and environment support, and make sure you understand the code rather than accepting generated solutions blindly.
Do not assume every low-level detail needs to be handled the way it would be in C++. Python intentionally hides more of the implementation, and that is often useful when the libraries involved delegate expensive work to native code. Learn enough about the runtime to diagnose problems, but also learn where Python's abstractions are appropriate and where performance, typing, or explicit resource management require extra care.
Since you already know how to program, start with the official Python tutorial instead of a beginner programming course. Pay particular attention to Python's data model, iterators and generators, context managers, exceptions, modules and packages, virtual environments, and type hints. The syntax is easy; learning the conventions and best practices is the more important part.
A structured book or course can be valuable, especially after using an AI coding tool heavily. Work through examples by typing and modifying them yourself so you build a foundation instead of passively reviewing generated code. AI is still useful as a supplement: ask it to clarify an explanation, suggest examples, or compare approaches, but keep yourself responsible for the design and verify what it produces.
For a practical project setup, tools worth investigating include uv for Python versions, environments, dependencies, and lockfiles; Ruff for formatting and linting; Pyright for static type checking; pytest for tests; and Rope or an IDE's refactoring tools. These guardrails improve consistency for both human-written and AI-generated code. Python's interactive REPL is also handy for quickly testing language features or exploring a library without turning every experiment into a full program.
Those tools are especially useful for controlling the flexibility of Python. Automated formatting, type checking, tests, and pinned dependencies make it much easier to maintain a project and review AI-generated changes.
That makes sense. I am less concerned about learning basic syntax than about developing good Python habits instead of writing C# or C++ in a different-looking language.