Python Programming Roadmap

A structured roadmap for going from Python beginner to expert, organised by skill level.

Basic

1. Variables and data types
2. Basic data structures: lists, tuples, sets, dictionaries
3. Boolean conditions and comparisons
4. Chained and compound conditions (and, or, not)
5. Operators: +, *, %, /, //, **
6. Control flow: if / elif / else
7. Loops: for and while over lists, dicts, ranges
8. Functions: definition, arguments, return values
9. Mutable vs immutable types
10. Common methods: list operations, string methods (.upper(), .lower(), .split(), .join())
11. String formatting: f-strings, .format()
12. I/O operations: reading and writing files

Intermediate

1. Object-oriented programming: classes, inheritance, encapsulation
2. Exception handling: try / except / finally, custom exceptions
3. Data structures: stacks, queues, linked lists
4. Comprehensions: list, dict, set, generator expressions
5. Lambda functions
6. collections module: defaultdict, Counter, deque, namedtuple
7. *args and **kwargs
8. Dunder methods: __init__, __repr__, __len__, __eq__, etc.
9. map(), filter(), zip(), enumerate()
10. itertools module
11. Type hints and annotations
12. pip and dependency management
13. Virtual environments: venv, conda
14. Modules and packages: imports, __init__.py, relative imports
15. Asyncio: async/await, event loops, asynchronous programming

Advanced

1. Decorators: function and class decorators, functools.wraps
2. Generators and yield: lazy evaluation, itertools pipelines
3. Context managers: with statement, __enter__ / __exit__, contextlib
4. Metaclasses: type, custom metaclasses, __new__
5. Concurrency and parallelism: threading, multiprocessing, concurrent.futures
6. Logging: logging module, handlers, formatters, log levels
7. Profiling and optimisation: cProfile, timeit, memory profiling
8. Testing: unittest, pytest, mocking, fixtures, coverage

Expert

1. Building and publishing packages: pyproject.toml, setuptools, PyPI
2. CPython internals: interpreter, bytecode, the GIL, reference counting, garbage collection
3. Extending Python with C: ctypes, cffi, Cython
4. Descriptor protocol: __get__, __set__, __delete__
5. Import system internals: importlib, custom finders and loaders
6. Specialised ecosystems: PyTorch, TensorFlow, NumPy internals, Django/FastAPI