PYTHON SERIES · 46 lessons

Learn what Python is, install Python 3.10+ and VS Code, run your first print statement, and understand the difference between REPL and script mode.

Explore Python's four core data types, learn how to assign variables, use type() to inspect values, and perform basic type conversions.

Master Python's arithmetic, comparison, and logical operators, format output with f-strings, and build interactive programs with input().

Control program flow with if/elif/else blocks. Learn nested conditions, the ternary expression, and how to structure complex decision logic.

Automate repetitive tasks with for and while loops. Master range(), break, continue, and the rarely-known loop else clause.

Work with Python's most-used sequence types. Master list CRUD operations, slicing syntax, common list methods, and tuple unpacking.

Use dictionaries for key-value storage and sets for unique collections. Learn dict comprehensions, set algebra, and when to choose each structure.

Strings are immutable sequences with a rich method library. Learn to slice, search, replace, split, join, and format text for real-world use.

Functions let you name and reuse blocks of logic. Learn def, return, default arguments, keyword arguments, and variable scope (LEGB rule).

Combine everything from weeks 1–9 into a full command-line to-do application with add, list, complete, and delete commands. Optionally persist tasks to a JSON file.

Go beyond basic def: write anonymous functions with lambda, understand closures, handle variable-length arguments, and use functools tools like partial and lru_cache.

Write concise, readable data transformations with list, dict, and set comprehensions. Then step into generators and yield for memory-efficient iteration.

Organize code into reusable modules and packages. Understand how Python's import system works, create your own packages, and manage dependencies with pip.

The Python standard library has batteries included. Learn the most useful modules: datetime for time, pathlib for files, collections for advanced data structures, and itertools for efficient looping.

Write robust programs by handling errors gracefully. Learn the full try/except/else/finally pattern, re-raise exceptions, and define custom exception classes.

Persist data to disk with Python's file I/O. Read and write plain text, CSV, and JSON files using context managers, the csv module, and json module.

Model real-world entities with classes. Learn to define attributes in __init__, write instance methods, use properties for validation, and simplify data classes with @dataclass.

Extend classes with inheritance, call parent methods with super(), implement polymorphism, and customize object behavior with dunder (magic) methods.

Unlock powerful text-matching with regular expressions. Learn pattern syntax, groups, lookahead/lookbehind, and use re.findall, re.sub, and re.fullmatch for text extraction and cleaning.

Combine the intermediate track skills to build a command-line text analysis tool that reads files, tokenizes text, computes statistics, and produces a formatted report.

Add static type information to Python code with type hints, use TypeVar for generic functions, model data with @dataclass, and define structural subtypes with Protocol.

Master decorators for cross-cutting concerns like logging, caching, and timing. Then implement reusable context managers with both the class-based and contextlib.contextmanager approaches.

Implement the iterator protocol from scratch, write memory-efficient generators with yield, compose pipelines with yield from, and use generator's send() for two-way communication.

Write concurrent programs with threading and multiprocessing. Understand the GIL, synchronize shared state with Lock and Queue, and use concurrent.futures for a clean high-level API.

Write asynchronous programs with asyncio. Understand coroutines, tasks, the event loop, and use aiohttp for non-blocking HTTP requests.

Write professional tests with pytest. Use fixtures for setup/teardown, parametrize for data-driven tests, mock external dependencies, and measure code coverage.

Extract structured data from web pages ethically. Use requests for HTTP and BeautifulSoup for parsing HTML. Implement rate limiting, respect robots.txt, and cache results.

Accelerate numerical computation with NumPy arrays and explore tabular data with Pandas. Combine them with Matplotlib to visualize results.

Turn your Python code into a distributable package. Learn pyproject.toml, build tools (hatch/setuptools), create CLI entry points, and publish to PyPI (or TestPyPI).

Combine the full advanced track into a production-grade async REST API client library, complete with type hints, retry logic, caching, pytest test suite, and a pip-installable package.