Python Programming

Identifiers, keywords, indentation, comments, documentation strings, Unicode and encoding, data types and type hints, object identity vs equality, operator precedence and associativity, input/output (print formatting with f-strings, format(), old %), command-line arguments using sys.argv, control flow: if, elif, else, match statement (PEP 634), loops: for, while, break, continue, else with loops, iterator protocol (__iter__, __next__), comprehensions (list, set, dict, nested), unpacking and zipping, enumerate, zip, range.

Defining and calling functions, arguments: positional, keyword, default, variable-length (*args, **kwargs), recursion, anonymous functions with lambda, map, filter, reduce, any, all, sorted, min, max with custom keys, first-class functions, closures, decorators (basic and with arguments), generators (yield, send), iterators vs generators, import system, module search path, creating and importing user-defined modules, packages and __init__.py, virtual environments and dependency management, built-in collections: list, tuple, dict, set, frozenset, Counter, defaultdict, OrderedDict, deque.

Classes and objects, class and instance attributes, methods (self), constructor (__init__), attribute access control, class variables and methods (@classmethod, @staticmethod), inheritance, method overriding, composition vs inheritance, super(), abstract classes and interfaces using abc, operator overloading (__add__, __eq__, etc.), duck typing, multiple inheritance, MRO (Method Resolution Order), introspection (dir(), hasattr(), getattr()), exception hierarchy, custom exceptions, structured exception handling (try-except-else-finally), resource management with with, file I/O, JSON and CSV processing, pickling, error and exception best practices.

Modeling with Python data structures (lists, stacks, queues, dictionaries, sets), simulation problems, trees: node- based and list-based representations, binary tree traversal, recursion vs iteration, search and sorting using Pythonic methods, time and space complexity analysis using empirical profiling, introduction to timeit and cProfile, unit testing (unittest, doctest, pytest), assertions, type checking (mypy, typeguard), type annotations and gradual typing (PEP 484), introduction to NumPy arrays, basic file-based or JSON-based persistence, project structure, naming conventions (PEP 8), packaging and publishing modules, documentation with pydoc and sphinx.